DESC:SYSTEM FOR DETECTING VIDEO-BASED FRAUD AND THE METHOD THEREOF
Field of the invention:
The present invention discloses a system and method for detecting video-based fraud, utilizing a plurality of independently operating, modality-specific machine learning models that execute on local or edge computing infrastructure. The system preprocesses video content locally to extract visual, audio, and physiological features and generates respective confidence or anomaly scores via deep learning models trained on spatial, temporal, and biometric data. These scores are aggregated using a Bayesian or probabilistic fusion algorithm to compute a final Trust Factor and Accuracy Score. The system is stateless, privacy-preserving, and compliant with regulatory frameworks such as GDPR, HIPAA, and CCPA, and does not store or transmit any user video or biometric data. Deployment supports on-premises or hybrid infrastructure via a secured API or SDK.
The present inventive concept, by its special mechanism of analyzing a video, tries to address several technical issues, among others, including Synthetic frauds, especially deepfakes, pose major threats in finance, law enforcement, insurance, and digital communications.
This disclosure relates to systems and methods for synthetic fraud detection, particularly in detecting AI-generated deepfakes in video media. Existing systems suffer from low multimodal fusion accuracy, limited real-time capability on edge devices, and reliance on centralized cloud processing that compromises user privacy. The invention addresses these by providing a decentralized, privacy-preserving AI framework for authenticity verification using parallel inference across visual, biometric, and audio modalities.
The present system is so constructed as a privacy-preserving, multimodal AI system to detect such synthetic frauds without ever accessing user video content directly (running on the company's cloud).
The system applies a multilayer AI pipeline, where each specialized model extracts micro-signals of authenticity and detects anomalies linked to synthetic manipulations.
Background of the Invention:
As high quality video capturing devices, such as those implemented within mobile phones, are widely available nowadays, digital videos have become an increasingly popular tool for recording and/or reporting events.
However, sophisticated video editing techniques, such as Deepfake, that use artificial intelligence to synthesize human images, pose a threat to the credibility of videos. These techniques enable users to easily superimpose images of one person (e.g., the face of a person) onto images or videos that show bodies of another person in a manner that is not easily detectable by human eyes. These techniques have been used by malicious users to manipulate existing media to generate content that is deceptive, for example, generating fake news. Without readily available tools that can determine the authenticity of a video, it may be challenging for the public to detect that the content of the video has been previously modified. Thus, there is a need for effectively and accurately detecting and/or preventing modifications of digital content.
A Prior Patent US US11023618B2 relates to techniques for digital video authentication (and preventing fake videos). First pixels within a first image frame of the video clip representing an area of interest within the first image frame may be identified. The area of interest may correspond to a person's face or another object. A first frame signature may be calculated based on the first pixels. Second pixels within a second image frame of the video clip representing an area of interest within the second image frame may be identified. A second hash value may be calculated based on the second pixels. The authenticity of the video clip may be determined by comparing the first and second hash values against data extracted from third pixels within the first image frame that do not correspond to the area of interest in the first image frame.
Another prior art CN111241958B describes a video image identification method based on a residual-capsule network, which belongs to the image classification technology in the field of computer vision image processing. This method constructs a residual-capsule neural network through a residual neural network that extracts the latent features of the image, a capsule network that encodes the correspondence between local and whole objects, and a decoder that reconstructs the image, and mainly solves the problem of overfitting in convolutional neural networks. , the problem of gradient disappearance, at the same time, the original input image is reconstructed through the output vector of the capsule network, and the model is discriminated and classified according to the matching degree between the reconstruction and the original image, which further improves the detection performance of forged face images and videos.
Further another prior art US11695975 relates to System and method for live web camera feed and streaming transmission with definitive online identity verification for prevention of synthetic video and photographic images. The prior art discloses a live web camera feed and streaming transmission system and method for gathering, identifying and authenticating biometric data of a specific human being while constantly monitoring, tracking, analyzing, storing and distributing dynamic biometric data to ensure authorized access to the secured system continues via positive live feed monitoring of biometric data for participating computer systems and or programs. Multiple, correlative, inseparable, embedded serial numbers allow for editing within a live video recording session because the serial numbers are “attached” to one another from frame to frame. The degree of identity verification correlated with the various serial numbers, directly affects an indelible, detectible, identity verification cumulative authentication rating score in conjunction with a recognizable and standardized, indelible, detectible, hyperlinked color-coded security badge displaying the degree of identity authentication. If any one of these components that work cooperatively and correlatively is tampered with in any regard, the video is rendered inoperable.
Another prior art US20190164173A1 demonstrate a fraud detection computing system comprising:
• a contributor external-facing device configured for communicating with a fraud detection server system through a security portal and for obtaining, via communications with contributor computing systems over a public data network, transaction data and account data for online entities;
• a client external-facing device configured for:
? receiving, from a client computing system and during a target transaction between the client computing system and a consumer computing system, a query regarding a presence of a fraud warning for a target consumer associated with the consumer computing system, and
? transmitting, prior to completion of the target transaction, the fraud warning to the client computing system,
• in a secured part of the fraud detection computing system:
? an identity repository to securely store the account data and the transaction data obtained from the contributor computing systems; and
• the fraud detection server system configured for:
? generating, in a data structure and based, at least in part, upon the account data and the transaction data, entity links between primary entity objects identifying primary entities for a plurality of accounts and a secondary entity object identifying the target consumer as a secondary entity for the plurality of accounts, the entity links including persistent associations in the data structure between the primary entity objects and the secondary entity object such that a relationship between the primary entity objects and the secondary entity object is represented in response to at least one of the primary entity objects and the secondary entity object being accessed,
? correlating values between attributes of the secondary entity object and attributes of the primary entity objects,
? detecting, based on the correlation, an inconsistency between a combination of a name attribute value and an address attribute value of the secondary entity object as compared to the primary entity objects, a name attribute identifying a family name for an entity and an address attribute identifying a physical address for the entity, and
? generating, responsive to the query, the fraud warning based on the inconsistency.
One other prior art CN113723220B discloses a deep counterfeit traceability system based on a big data federated learning framework, including an application layer, an interface layer, a logic layer, a network layer, and a storage layer connected in sequence; the application layer is used to provide users with deep counterfeit traceability services, and Obtain user login and upload data; the interface layer is used to provide interface services and realize communication between the server and the web; the logic layer is used to divide system functions, and design algorithms to build models to realize system function logic; the network layer is used to exchange parameters , and encrypt the gradient information during the modeling process; the storage layer is used to receive the transmitted parameter information and encrypted information, and store them in the local database and blockchain network. The invention proposes the overall structure of the federated anti-counterfeiting traceability chain, establishes a triple mechanism of federated anti-counterfeiting, abnormal traceability, and risk prediction, which can effectively solve the problems of data poisoning and single-point failure for federated learning while preventing Web security threats.
In another prior art US11727721, Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.
Even though there are several prior arts relating to video authentication and deepfake detection systems are known, still more effective and safe inventive concept in the concerned technical field is required, particularly due to very fast changing computer and internet related technologies. The proliferation of sophisticated AI-generated synthetic media, commonly known as deepfakes, poses significant challenges to the authenticity and trustworthiness of digital content across various domains, including finance, law enforcement, national security, and social media. Existing systems for detecting such manipulations often suffer from several limitations.
For instance, many prior art systems (e.g., as generally described in CN111241958B focusing on residual-capsule networks, or US 11,727,721 B2 which details methods based on eye aspect ratio and local binary patterns) primarily focus on a limited set of visual artifacts or unimodal analysis. Such approaches may be susceptible to adversarial attacks that specifically target those limited features and often fail to capture the subtle, multimodal inconsistencies present in sophisticated synthetic media. These systems may lack the robustness required for reliable operation across diverse, real-world video conditions, such as low-quality recordings, occlusions, or varying lighting.
Furthermore, existing solutions often require the transmission of user data, including raw video or extracted features, to centralized cloud servers for processing. This raises significant privacy concerns and may not comply with stringent data protection regulations like GDPR, HIPAA, or CCPA, and data sovereignty requirements. While federated learning approaches (as generally discussed in CN113723220B) aim to address some privacy concerns by training models without sharing raw data, the inference process itself in many systems may still rely on centralized components or may not be optimized for real-time, on-device execution of a comprehensive suite of detection models.
Additionally, many existing deepfake detection systems operate as "black boxes," providing a binary classification (real/fake) or a simple confidence score without sufficient explanation of the underlying reasoning. This lack of transparency and interpretability limits their utility in critical decision-making processes where justification is paramount.
Moreover, prior art often focuses on static or frame-wise analysis, neglecting the rich temporal dynamics and behavioral cues inherent in genuine human expression and interaction. For example, methods relying solely on facial geometry or simple blink rates may miss inconsistencies in the temporal evolution of expressions, gaze patterns, or physiological signals that are difficult for current generative models to replicate accurately and congruently across modalities.
Existing video authentication and deepfake detection systems rely heavily on single-modality analysis and centralized cloud-based inference, which pose risks in scalability, privacy, and real-time performance. These systems fail to robustly detect multimodal inconsistencies and are susceptible to synthetic fraud, especially in sensitive industries like finance, insurance, law enforcement, and digital identity verification.
Therefore, there is a pressing need for an improved system and method that can robustly, accurately, and in real-time detect synthetic media and assess behavioral trust, while operating in a privacy-preserving manner and providing explainable results. Such a system should overcome the limitations of unimodal analysis, centralized processing, and lack of interpretability found in the prior art.
Prime oobject of the present invention is to provide a system and method for detecting synthetic video-based fraud, utilizing a plurality of independently operating, modality-specific machine learning models that execute on local or edge computing infrastructure.
Another prime object of the present invention to provide the Adaptive Cognito Engine (ACE), a core AI engine that uniquely integrates and orchestrates a plurality of distinct, specialized AI inference modules.
Another object of the present invention is to provide such a systems and methods for analyzing digital media content, and more particularly, to a system and method for real-time, multimodal trust verification and the detection of synthetic media, including deepfakes, while preserving user privacy.
Another object of the present invention is to provide a method for detecting synthetic video-based fraud and for analyzing digital media content; wherein the system preprocesses video content locally to extract visual, audio, and physiological features and generates respective confidence or anomaly scores via deep learning models trained on spatial, temporal, and biometric data.
Another object of the present invention is to provide such a system and method for detecting synthetic video-based fraud and for analyzing digital media content; wherein the system is stateless, privacy-preserving, and compliant with regulatory frameworks such as GDPR, HIPAA, and CCPA, and does not store or transmit any user video or biometric data.
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) and the method of its operations which overcomes the aforementioned limitations of the prior art, wherein said ACE is characterized as a novel multimodal machine learning framework designed for the real-time, privacy-preserving analysis of short video segments (e.g., 5-30 seconds) to determine behavioral trust, content authenticity, and detect synthetic media manipulations, including deepfakes.
Another object of the present invention is to provide a decentralized, privacy-focused AI system capable of evaluating at least a 30-second video, preferably a 30-second video, using multiple independently operating AI models that assess different modalities; wherein each model produces a score reflecting visual, auditory, or biometric authenticity and wherein such scores are fused using a statistical engine to compute a final Trust Factor and Accuracy Score.
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) and the method of its operations; wherein within ACE, each of said AI inference modules processes the input video segment concurrently and independently and the architecture of strict independent parallel processing thereof is the characteristic aspect of ACE, ensuring that the analysis of one modality is not prematurely biased or influenced by another during the initial feature extraction and inference stages, thereby minimizing error propagation and enhancing robustness against sophisticated adversarial attacks.
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) and the method of its operations; wherein said ACE incorporates a Hierarchical Trust Fusion Engine, which firstly receives the independent outputs (e.g., confidence scores, anomaly metrics, feature vectors) from all constituent AI modules, and then the said Trust Fusion Engine intelligently aggregates these diverse outputs using advanced statistical methods, which may include, but are not limited to, weighted Bayesian inference, ensemble learning (e.g., XGBoost), or dynamically adaptive algorithms. The output of this fusion process is a holistic "Trust Factor" (e.g., on a 1-10 scale) representing the overall assessed authenticity and trustworthiness of the subject or content in the video, and an "Accuracy Score" indicating the system's confidence in that Trust Factor.
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) and the method of its operations; wherein said ACE is architected for inherently privacy-preserving operation, primarily through edge-first processing; wherein the entire ACE framework, including all its AI inference modules and the Trust Fusion Engine, is designed for deployment and execution locally on a user's device, within a client's on-premise infrastructure, or on a dedicated, secure edge appliance (e.g., the conceptualized "FOAI Box").
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) and the method of its operations; wherein the Ocular Dynamics Analysis Module (FETM) within ACE embodies novel techniques for eye-based behavioral and biometric analysis which includes, but are not limited to, the analysis of microsaccades via a Microsaccadic Transformer Engine, the calculation of a Blink-Incongruence Score (BIS) by correlating blink dynamics with cross-modal emotional cues, the simulation of physiological pupil responses via a Neuro-Ocular Reflex Simulator (NORS), the detection of pupil oscillations via Differential Pupil Oscillation Mapping (DPOM), and the assessment of inter-eye congruence via a Bilateral Ocular Congruence Engine (BOCE). These granular analyses are specifically designed for robustness in low-quality and compressed video, representing an inventive step over prior art methods relying on simpler ocular metrics.
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) to provide Explainable AI (XAI) capabilities, wherein the Trust Factor generated by ACE is accompanied by justifications, which may include per-module confidence contributions, visual heatmaps highlighting salient regions or temporal segments, and identification of specific behavioral or biometric cues that most significantly influenced the outcome.
Another object of the present invention is to provide an Adaptive Cognito Engine (ACE) to utilize Nature-Inspired Optimization (NIO) algorithms (e.g., Grasshopper Optimization, Particle Swarm Optimization) for the adaptive tuning of hyperparameters within its constituent AI modules or the Trust Fusion Engine, thereby enhancing performance and robustness across diverse and dynamically changing real-world conditions.

Summary of the Invention:
The present invention provides a decentralized, privacy-focused AI system capable of evaluating short video content, preferably a 5 to 30 second video, using multiple independently operating AI models that assess different modalities. Each model produces a score reflecting visual, auditory, or biometric authenticity. These scores are fused using a statistical engine to compute a final Trust Factor and Accuracy Score. The system functions without storing video content or transmitting personal data, ensuring full compliance with GDPR, HIPAA, and CCPA.
The present invention is directed to an Adaptive Cognito Engine (ACE), hereinafter also referred to as "Faceoff AI" or "FOAI," which overcomes the aforementioned limitations of the prior art. ACE is a novel multimodal machine learning framework designed for the real-time, privacy-preserving analysis of short video segments (e.g., 5-30 seconds) to determine behavioral trust, content authenticity, and detect synthetic media manipulations, including deepfakes.
It is the primary object of the present invention to provide the Adaptive Cognito Engine (ACE), a core AI engine that uniquely integrates and orchestrates a plurality of distinct, specialized AI inference modules.
In an exemplary embodiment, ACE comprises eight (8) such modules, each architected to independently analyze different behavioral and biometric cues from an input video segment. These cues and corresponding modules include, but are not limited to:
i. A Deepfake Artifact Detection Module for analyzing visual and frequency-domain inconsistencies indicative of synthetic generation.
ii. A Facial Emotion Analysis Module for recognizing and assessing the congruence of facial expressions.
iii. An Ocular Dynamics Analysis Module (FETM), a specialized component of ACE, for granularly analyzing advanced eye-tracking biometrics, blink kinematics, pupil reflexes, and micro-expressions around the ocular region.
iv. A Posture and Gesture Analysis Module for interpreting body language and motion dynamics.
v. A Speech Sentiment Analysis Module for understanding the emotional content of spoken language using Natural Language Processing.
vi. An Audio Tone and Prosody Analysis Module for evaluating vocal characteristics indicative of stress or deception.
vii. A Remote Photoplethysmography (rPPG) Module for contactless estimation of physiological signals such as heart rate variability.
viii. A Blood Oxygen Saturation (SpO2) Estimation Module for contactless assessment of another key physiological indicator.
One of the most important aspects of the present invention is that within ACE, each of said AI inference modules processes the input video segment concurrently and independently. This architectural principle of strict independent parallel processing is a core inventive aspect of ACE, ensuring that the analysis of one modality is not prematurely biased or influenced by another during the initial feature extraction and inference stages, thereby minimizing error propagation and enhancing robustness against sophisticated adversarial attacks.
Another technical aspect of the present invention is that the said ACE incorporates a Hierarchical Trust Fusion Engine. This Hierarchical Trust Fusion Engine receives the independent outputs (e.g., confidence scores, anomaly metrics, feature vectors) from all constituent AI modules. The Trust Fusion Engine then intelligently aggregates these diverse outputs using advanced statistical methods, which may include, but are not limited to, weighted Bayesian inference, ensemble learning (e.g., XGBoost), or dynamically adaptive algorithms. The output of this fusion process is a holistic "Trust Factor" (e.g., on a 1-10 scale) representing the overall assessed authenticity and trustworthiness of the subject or content in the video, and an "Accuracy Score" indicating the system's confidence in that Trust Factor. This nuanced, multi-faceted trust assessment is a significant departure from simpler binary classifiers or unimodal scoring systems of the prior art.
Another significant technical aspect of the present invention is that the said ACE is architected for inherently privacy-preserving operation, primarily through edge-first processing. The entire ACE framework, including all its AI inference modules and the Trust Fusion Engine, is designed for deployment and execution locally on a user's device, within a client's on-premise infrastructure, or on a dedicated, secure edge appliance (e.g., the conceptualized "FOAI Box"). During its primary operation of analyzing a video segment and generating a Trust Factor, ACE does not transmit raw video data, detailed extracted features, or sensitive biometric information to any external or centralized Faceoff-controlled servers. This design ensures compliance with stringent data protection regulations (e.g., GDPR, HIPAA, CCPA, DPDP Act) and data sovereignty mandates by keeping sensitive data within the control of the data owner.
Another important technical aspect of the present invention is the Ocular Dynamics Analysis Module (FETM) within ACE embodies novel techniques for eye-based behavioral and biometric analysis which includes, but are not limited to, the analysis of microsaccades via a Microsaccadic Transformer Engine, the calculation of a Blink-Incongruence Score (BIS) by correlating blink dynamics with cross-modal emotional cues, the simulation of physiological pupil responses via a Neuro-Ocular Reflex Simulator (NORS), the detection of pupil oscillations via Differential Pupil Oscillation Mapping (DPOM), and the assessment of inter-eye congruence via a Bilateral Ocular Congruence Engine (BOCE). These granular analyses are specifically designed for robustness in low-quality and compressed video, representing an inventive step over prior art methods relying on simpler ocular metrics.
Another technical aspect for ACE to provide Explainable AI (XAI) capabilities. The Trust Factor generated by ACE is accompanied by justifications, which may include per-module confidence contributions, visual heatmaps highlighting salient regions or temporal segments, and identification of specific behavioral or biometric cues that most significantly influenced the outcome. This transparency in decision-making is a critical advancement over opaque prior art systems.
Another technical aspect for ACE to utilize Nature-Inspired Optimization (NIO) algorithms (e.g., Grasshopper Optimization, Particle Swarm Optimization) for the adaptive tuning of hyperparameters within its constituent AI modules or the Trust Fusion Engine, thereby enhancing performance and robustness across diverse and dynamically changing real-world conditions.
In one exemplary embodiment, a method for real-time multimodal trust verification and synthetic media detection, implemented by the Adaptive Cognito Engine (ACE), comprises:
• receiving a short video segment at a local computing environment;
• concurrently and independently analyzing said video segment via a plurality of distinct AI inference modules integrated within ACE,
• each module configured to extract and analyze features from a unique behavioral or biometric cue selected from a comprehensive set including visual forgery artifacts, facial emotion, ocular dynamics, posture, speech sentiment, audio tone, heart rate, and blood oxygen saturation,
• each module thereby generating a modality-specific output; and
• aggregating said modality-specific outputs within ACE using a hierarchical statistical fusion algorithm to compute a final Trust Factor and an associated Accuracy Score, wherein said analyzing and aggregating steps are performed substantially within said local computing environment.
The Adaptive Cognito Engine (ACE) of the present invention, with its unique architecture of independently operating yet holistically fused multimodal AI modules, its inherent privacy-preserving edge-first processing design, its advanced and granular analysis within specialized components like the FETM, its integrated XAI capabilities, and its potential for adaptive optimization via NIO, provides a system and method that is significantly more robust, accurate, private, explainable, and adaptable than those found in the prior art. ACE directly addresses the critical and unmet needs in the rapidly evolving field of synthetic media detection and behavioral trust assessment in digital interactions.
Workflow for Synthetic Fraud Analysis
1. Video Input: A company processes a 30-second video locally using the Adaptive Cognito Engine (ACE) of the present invention.
2. Preprocessing:
• Frames extraction
• Audio track separation
• Resolution normalization
• Lighting adjustment for consistency.
3. Multimodal Feature Extraction (each model independently operates):
• Visual Features (Deepfake detection, Facial Emotion, Eye Tracking, Posture Analysis)
• Audio Features (Speech Sentiment, Audio Tone Sentiment)
• Biometric Features (Heart Rate, Oxygen Saturation)
4. Independent Model Inference:
• Each model processes extracted features and generates its own trust, anomaly, or sentiment scores.
5. Fusion Engine:
• Scores are fused into a Trust Factor and Final Accuracy Score without needing the video or its metadata.
6. Results:
• Delivered locally to the company. Only API usage (call count) is reported to the database of the present system.
Advantages Over Prior Art:
• Real-time processing without cloud dependence
• True multimodal analysis across 8 independent AI channels
• Fully stateless with zero data retention
• Biometric authentication without contact sensors
• Modular architecture enabling industry-specific scaling
How Each Model Detects Synthetic Frauds
a) Deepfake Detection (Visual Integrity Check)
• Spatial Artifact Detection:
o Detects subtle inconsistencies around facial regions (e.g., boundary artifacts, unnatural blending, edge inconsistencies).
• Temporal Motion Analysis:
o Irregularities in lip-sync, unnatural blinking, jittery head movement using CNN+RNN pipeline.
• Frequency Domain Inspection:
o Uses Fast Fourier Transform (FFT) on frame sequences to spot abnormal frequency artifacts typical in GAN-generated media.
• GAN Discriminator Techniques:
• Trained discriminators classify real vs synthetic textures and lighting patterns.
Key Strength: Deepfake module of the presently invented system looks at multi-scale temporal and frequency inconsistencies simultaneously, whereas many traditional detectors rely only on frame-level artifacts.
b) Facial Emotion and Eye Tracking (Behavioral Authenticity Signals)
• Microexpression Detection:
o Natural humans exhibit involuntary microexpressions; deepfakes often miss these slight eyebrow flicks, subtle grimaces, or minute asymmetries.
• Eye Behavior Analysis:
o Real humans blink between 2–10 seconds normally. Synthetic faces often blink less or in unnatural patterns.
o Presently invented system captures eye saccades, blink dynamics, and gaze stability.
Key Strength: Naturalness of emotion and eye behavior is extremely difficult for synthetic models to fake precisely over 30 seconds.
c) Posture and Biometric Analysis (Physiological Truth Layer)
• Posture Behavior:
o Sudden posture rigidness, missing fine shoulder motions, or unnatural stillness is caught.
• Heart Rate via rPPG:
o Deepfake videos often lack subtle skin pixel variations tied to real blood flow.
• SpO2 Estimation:
• Stress-induced oxygen variation analysis is almost impossible for current synthetic models to simulate naturally.
Key Strength: Biometric layer of the presently invented system measures life signals that synthetic frauds cannot replicate without specialized, very high-end simulation systems.
d) Speech Sentiment and Audio Tone Analysis (Voice-Based Authenticity)
• Audio-Visual Synchronization:
o Deepfakes struggle with perfect audio-lip sync; mismatches are flagged.
• Tone and Modulation Anomalies:
o Synthetic voices lack natural emotional tone patterns and stress modulations.
• Spectrogram Analysis:
• Speech spectrograms show irregularities when audio is AI-generated.
Key Strength: Presently invented system cross-validates the voice tone with the spoken sentiment and visual facial cues — a mismatch between voice emotion and facial emotion signals possible synthesis.
4. Deep Technical Strengths Unique to the presently invented system:
Conventional Deepfake Detection Faceoff Advanced Detection
Frame-level artifact detection Multimodal (visual + audio + biometric) cross-check
Single-model classification Independent multi-model validation
Passive feature observation Actively reconstructs trust timelines, event patterns
No biometric validation Live biometric signal integration (heart rate, SpO2)
Data-dependent cloud models Fully local, privacy-preserving, with usage-only reporting

5. Privacy and Security in Synthetic Fraud Detection
• Company retains 100% control over their video and data.
• Faceoff API operates locally — Faceoff server only sees API usage (how many videos processed, not the video itself).
• Data is neither transferred nor stored by Faceoff at any point.
• Stateless design — videos processed in memory and immediately discarded.
• Compliant with GDPR, HIPAA, CCPA — no personal data exposure.
6. Real-World Example Scenario
Use Case:
In a specific illustrative scenario, a major insurance company internally uses the presently invented system and its methods for deepfack video detection and analysis thereof.
• A customer submits a claim with a suspicious injury video.
• The company's local system API, as described for the instant invention:
o Flags the lip-sync mismatch.
o Detects missing micro-blinks.
o Notices unnatural body posture shifts.
o Observes stable but synthetic SpO2/heart patterns.
• Final Trust Score = 2.5/10 (Very Low).
• The insurance agent reviews the results internally, rejecting the fraudulent claim — no video ever leaves their secure server.
The presently invented system and the method delivers state-of-the-art synthetic fraud detection through a modular, privacy-first, multimodal AI engine that cross-verifies emotional, biometric, visual, and audio signals — all while respecting user data privacy and strengthening digital trust infrastructures.
• No video leak risk.
• Multi-signal cross-validation.
• Zero cloud dependency.
• High robustness against evolving deepfake technologies.
SHORT DETAILS OF THE DRAWINGS:-
FIG. 1: Conceptual System Architecture Diagram
FIG. 2: Schematic Flow-Chart, representing the Internal Operational Flow and Data Processing within ACE
FIG. 3: Conceptual Drawing towards illustrating an exemplary Deepfake Analysis Workflow & Decision Matrix.
Hardware Components within the Main Bounding Box (Figure 1):
1. Input Reception Hardware (110A):
o Position: Left side, where input enters.
o Shape: Rectangle.
o Label: "Input Reception Hardware (110A)"
o Examples (can be listed inside or as callouts): "Camera Interface (e.g., MIPI CSI-2, USB)", "Network Interface Card (NIC)", "File System Controller".
o Arrow: An arrow from outside labeled "Input Video Segment (112)" points to this.
2. Central Processing Unit(s) (CPU) (122):
o Position: Central within the processing area.
o Shape: Rectangle.
o Label: "Central Processing Unit(s) (CPU) (122)"
o Description (optional): "System Orchestration, Preprocessing, AI Module Execution (some), Fusion Logic".
3. Specialized Processing Accelerators (GPU/TPU/NPU) (124):
o Position: Adjacent to or closely coupled with CPU (122).
o Shape: Rectangle.
o Label: "Hardware Accelerators (GPU/TPU/NPU) (124)"
o Description (optional): "Primary AI Model Inference Execution".
4. System Memory (RAM) (126):
o Position: Connected to CPU (122) and Accelerators (124).
o Shape: Rectangle.
o Label: "System Memory (RAM) (126)"
o Description (optional): "Temporary Storage for Models, Video Frames, Intermediate Data".
5. Non-Volatile Storage Device (130):
o Position: Accessible by the CPU (122).
o Shape: Cylinder or Rectangle.
o Label: "Non-Volatile Storage (e.g., SSD) (130)"
o Description (optional): "Stores Operating System, ACE Software Modules (including AI models), Configuration Data, Secure Anonymized Logs".
6. Output Generation Hardware (140A):
o Position: Right side, where output exits.
o Shape: Rectangle.
o Label: "Output Generation Hardware (140A)"
o Examples: "Network Interface Card (NIC)", "Display Controller (if local display)".
o Arrow: An arrow from this component points outside, labeled "Final Output (Trust Factor, etc.) (142)".
7. Secure Co-processor (Optional) (150):
o Position: Shown interacting with CPU (122) and Storage (130).
o Shape: Rectangle with a "secure" or "lock" icon.
o Label: "Secure Co-processor (e.g., TPM, Secure Enclave) (150)"
o Description (optional): "Cryptographic Operations, Key Storage, Integrity Verification".
Software Modules (Conceptually shown as executed by/on the hardware):
• A larger conceptual block labeled "ACE Software Modules (128) (Executing on CPU 122 & Accelerators 124, Loaded from Storage 130 into RAM 126)" can encompass:
o Input Validation & Buffering Controller (Software part of 110)
o Preprocessing Software Module
o Eight (8) distinct AI Inference Software Modules (128a-h)
o Trust Fusion Engine Software Module
o Explainable AI (XAI) Software Module
o Output Formatting & API Controller (Software part of 140)
Data Flow Arrows:
• Input Video Segment (112) ? Input Reception Hardware (110A) ? CPU (122) (for Preprocessing Software Module).
• CPU (122) (Preprocessing Output) ? GPU/TPU/NPU (124) & CPU (122) (for AI Inference Software Modules 128a-h).
• GPU/TPU/NPU (124) & CPU (122) (AI Module Outputs) ? CPU (122) (for Trust Fusion Engine & XAI Software Module).
• CPU (122) (Final Results) ? Output Generation Hardware (140A) ? Final Output (142).
• Double arrows between CPU/Accelerators and RAM (126), and between CPU and Storage (130).
DETAILED DESCRIPTION OF THE INVENTION
The present invention provides a system and method, embodied in an Adaptive Cognito Engine (ACE), for the real-time, privacy-preserving multimodal analysis of video segments to determine behavioral trust and detect synthetic media manipulations. The following description details the construction of the ACE system and its method of operation. Reference is made to conceptual figures (e.g., FIG. 1 - System Architecture, FIG. 2 - Internal Operational Flow, as previously described).
Referring now to FIG. 1 (Conceptual System Architecture Diagram – described below, as it would be a drawing in a patent), an exemplary embodiment of the presently invented system (100) is depicted. The system (100) is designed to operate either on a dedicated edge appliance (102) (e.g., the "FOAI Box"), or distributed across a client's existing on-premise infrastructure or private cloud, or directly on an end-user's sufficiently powerful computing device (104) (e.g., a high-end smartphone or workstation).
FIG. 1 - Conceptual System Architecture Diagram
• Input Interface/Receiver (110): This component is responsible for receiving the input short video segment (112) (e.g., 5-30 seconds).
o Hardware: This may include standard camera interfaces (e.g., USB, MIPI CSI-2) if integrated directly with a capture device, network interface cards (NICs) (e.g., Ethernet, Wi-Fi) for receiving video data from a network or an application, or file system interfaces for accessing stored video files.
o Software Controller: A software module, part of ACE, manages the input stream, ensuring the video data is correctly formatted and buffered for processing. It validates input parameters (e.g., video length, resolution, supported codecs like MP4, AVI, WebM).
o Functionality: The Input Interface/Receiver (110) acquires the video data and passes it to the Preprocessing Unit (120).
• Processing Unit (120) (Implementing the Adaptive Cognito Engine - ACE): This is the core of the system, typically comprising one or more processors and specialized hardware accelerators.
o Central Processing Unit(s) (CPU) (122): Standard multi-core CPUs (e.g., ARM64, Intel Xeon, AMD Ryzen) manage the overall workflow orchestration of ACE, handle certain preprocessing tasks, and may execute some AI model inference if specialized accelerators are unavailable or not optimal for a given module. The CPU also executes the final Trust Fusion Engine logic.
o Graphics Processing Unit(s) (GPU) / Tensor Processing Unit(s) (TPU) / Neural Processing Unit(s) (NPU) (124): Specialized hardware accelerators (e.g., NVIDIA GPUs like RTX series or Jetson AGX Orin, Google TPUs, or mobile NPUs) are utilized for computationally intensive tasks, primarily the inference operations of the eight (8) distinct AI modules within ACE. ACE is designed to leverage these accelerators for real-time performance (e.g., <50ms per module for deepfake detection, <100ms for multi-model analysis).
o Memory (RAM) (126): Sufficient Random Access Memory (e.g., 64-128 GB on an appliance, or system-dependent RAM on client infrastructure/devices) is required for holding the AI models, intermediate feature vectors, and buffered video frames during the stateless processing. ACE processes video in-memory to enhance speed and privacy, discarding frame data immediately after the relevant features are extracted and processed by the AI modules.
o ACE Software Modules (128) (Stored in Non-Volatile Storage and loaded into RAM):
? Preprocessing Module: Software for frame extraction (e.g., using OpenCV, FFmpeg), audio separation (e.g., using Librosa), resolution normalization, and lighting correction (e.g., histogram equalization).
? Eight Independent AI Inference Modules (128a-h): These are the core Deepfake Detection, Facial Emotion, FETM (Ocular Dynamics), Posture, Speech Sentiment, Audio Tone, rPPG, and SpO2 modules. Each module is a distinct software component, potentially comprising pre-trained neural network models (e.g., in ONNX, TensorRT, or PyTorch/TensorFlow format), statistical models, and associated feature extraction algorithms. These models are loaded into the memory (126) and executed on the appropriate processing hardware (122 or 124).
? Trust Fusion Engine Module: Software implementing the statistical aggregation algorithm (e.g., weighted Bayesian inference, ensemble learning) to compute the final Trust Factor and Accuracy Score from the outputs of modules (128a-h).
? XAI (Explainable AI) Module: Software components for generating justifications (heatmaps, feature importance scores) associated with the Trust Factor.
• Non-Volatile Storage (130):
o Hardware: Solid-State Drives (SSD, e.g., NVMe SSD of 2TB on an appliance) or other persistent storage.
o Content: Stores the operating system (e.g., hardened Linux), the ACE software including all AI models and the fusion engine, configuration files, and (optionally and securely, if configured by the client) anonymized API usage logs or XAI outputs for audit purposes. Crucially, raw input video data or identifiable biometric features are not persistently stored by ACE by default after processing.
o Database Controller (Conceptual): While ACE itself doesn't maintain a large user database of raw videos, it might interact with a minimal local configuration database (e.g., SQLite) for system settings or license management. If deployed in an enterprise, it may log anonymized statistical usage to a secure, designated database controlled by the client or Faceoff for billing/licensing, ensuring no PII is transmitted.
• Output Generator/Interface (140): This component is responsible for providing the results of the ACE analysis.
o Hardware: NICs for network transmission, or display interfaces if directly outputting to a screen.
o Software Controller: An ACE software module formats the output, which includes the Trust Factor, Accuracy Score, and optionally, XAI data (justifications, heatmaps).
o Format: Outputs are typically provided in a structured format like JSON via a secure REST API or SDK integrated into the client's application or system.
o Actionable Output: The output is designed to be directly usable by the client system for decision-making (e.g., flagging content, alerting an operator, denying access, or approving a transaction).
• Secure Enclave / TPM (Trusted Platform Module) (150) (Optional but preferred for high-security deployments, e.g., FOAI Box):
o Hardware: Specialized secure co-processor (e.g., ARM TrustZone, Intel SGX) or a TPM chip.
o Functionality: Protects cryptographic keys, model parameters (if encrypted at rest on storage 130), secure boot processes, and ensures the integrity of the inference logs and XAI outputs, preventing tampering.
Operational Flow (Conceptual Data Flow):
1. An Input Video Segment (112) is received by the Input Interface/Receiver (110) of the system (100 or 102 or 104).
2. The video segment is passed to the Preprocessing Module within the Processing Unit (120).
3. Preprocessed data (frames, audio streams) are concurrently fed to the Eight Independent AI Inference Modules (128a-h) executing on appropriate hardware (122, 124).
4. Each AI module outputs modality-specific scores/features to the Trust Fusion Engine Module.
5. The Trust Fusion Engine computes the final Trust Factor and Accuracy Score.
6. The XAI Module (if activated) generates justifications.
7. The Output Generator/Interface (140) formats and delivers these results (e.g., via API) to the requesting client application or system.
8. During this process, temporary data resides in Memory (126) and is discarded post-processing, ensuring stateless operation. Models and system software are loaded from Non-Volatile Storage (130).
9. Anonymized API call metadata (e.g., a counter for number of videos processed, associated API key, timestamp, but not the video itself or its content) might be logged to a designated secure location or transmitted for licensing and operational analytics, ensuring privacy of the core video data.
This constructional design ensures that ACE can perform its complex multimodal analysis with high speed and accuracy while adhering to strict privacy-preserving principles by processing data locally or at the edge, and by its stateless nature with respect to the input video content. The modularity of the AI components within ACE also allows for scalability and future enhancements.
FIG. 2: Internal Operational Flow and Data Processing within ACE
This flowchart illustrates the sequence of operations and data transformations within the Processing Unit (120) of the ACE system, highlighting hardware utilization and module interaction.
Start Block:
• Label: "Input Video Segment Received (from Input Interface 110)"
1. Preprocessing Stage (Executed primarily on CPU 122, can offload some tasks to GPU 124 if optimized):
• Box 1a: "Video De-Multiplexing & Frame Extraction"
o Input: Raw Video Segment
o Hardware Utilized: CPU (122) for control, potentially GPU (124) for hardware-accelerated decoding if available.
o Software Module: Preprocessing Module (part of ACE Software Modules 128)
o Process: Decode video, extract individual frames (e.g., at 30fps). Extract audio stream.
o Output: Sequence of Raw Video Frames, Raw Audio Stream.
o Arrow to Box 1b.
• Box 1b: "Frame Normalization & Enhancement"
o Input: Sequence of Raw Video Frames
o Hardware Utilized: CPU (122), GPU (124) for image processing tasks.
o Software Module: Preprocessing Module (128)
o Process: Resolution normalization (e.g., to 224x224 for CNNs), lighting correction (histogram equalization), color space conversion (e.g., BGR to RGB for models).
o Output: Sequence of Normalized Video Frames.
o Arrow to Stage 2.
• Box 1c (Parallel to 1b): "Audio Stream Processing"
o Input: Raw Audio Stream
o Hardware Utilized: CPU (122)
o Software Module: Preprocessing Module (128)
o Process: Convert to suitable format (e.g., WAV), extract features like MFCCs or spectrograms.
o Output: Processed Audio Features.
o Arrow to AI Modules requiring audio (e.g., Speech Sentiment, Audio Tone).
2. Parallel Multimodal Feature Extraction & AI Model Inference Stage (Primarily on GPU/TPU/NPU 124, orchestrated by CPU 122):
• Draw a central data bus or flow line from "Sequence of Normalized Video Frames" and "Processed Audio Features".
• Branching out from this central flow, show eight parallel processing paths, each representing one of the ACE AI Modules (128a-h). Each path contains:
o Box 2.X.i (e.g., 2.a.i for Deepfake Detection): "Modality-Specific Feature Extraction (Module X)"
? Input: Normalized Video Frames (and/or Processed Audio Features for relevant modules)
? Hardware Utilized: GPU/TPU/NPU (124) primarily, CPU (122) for some logic.
? Software Module: Specific AI Module (e.g., Deepfake Artifact Detection Module 128a)
? Process: Module-specific algorithms (e.g., CNN feature extraction, optical flow, landmark detection, rPPG signal extraction).
? Output: Intermediate Feature Vectors for Module X.
? Arrow to Box 2.X.ii.
o Box 2.X.ii (e.g., 2.a.ii): "Machine Learning Inference (Module X)"
? Input: Intermediate Feature Vectors for Module X
? Hardware Utilized: GPU/TPU/NPU (124) primarily.
? Software Module: Specific AI Module (e.g., pre-trained XceptionNet, Transformer, LSTM within 128a)
? Process: Feed features through the trained ML model to generate an initial score or classification.
? Output: Modality-Specific Score/Output_X (e.g., Deepfake_Probability, Emotion_Class, Gaze_Entropy_Value, HR_BPM).
? Arrow from each of the eight "Modality-Specific Score/Output_X" boxes converges to Stage 3.
Example for one path (Deepfake Detection - Module 128a):
o Input: Normalized Video Frames
o Box 2.a.i: "Visual & Frequency Artifact Feature Extraction"
o Box 2.a.ii: "Deepfake Classifier Inference (CNN/Transformer)"
o Output: Deepfake_Likelihood_Score_A
Example for FETM (Ocular Dynamics - Module 128c): This would be a more complex sub-flowchart itself as described previously, showing:
o "Eye Region Localization & Enhancement" -> "Ocular Landmark Extraction" -> "Temporal Gaze/Blink/Pupil Dynamics Modeling" -> "Ocular Anomaly Score (MOAS_C)"
3. Trust Fusion Stage (Executed on CPU 122, may use RAM 126 extensively for holding scores):
• Box 3: "Trust Fusion Engine"
o Input: All eight "Modality-Specific Score/Output_X" (from Stage 2).
o Hardware Utilized: CPU (122).
o Software Module: Trust Fusion Engine Module (part of ACE Software Modules 128).
o Process:
? Apply weights (potentially dynamic, based on input quality or context from an NIO module if included).
? Perform statistical aggregation (e.g., Bayesian inference, weighted averaging, ensemble classifier like XGBoost).
? Calculate overall Trust Factor (0-10) and Accuracy Score (system confidence).
o Output: Final Trust Factor, Final Accuracy Score.
o Arrow to Stage 4.
4. Explainable AI (XAI) Generation Stage (Can utilize both CPU 122 and GPU 124 for specific XAI techniques):
• Box 4: "XAI Module Processing"
o Input: Final Trust Factor, Intermediate Feature Vectors from AI Modules, Modality-Specific Scores, original Normalized Frames (for heatmaps).
o Hardware Utilized: CPU (122) for logic, GPU (124) for techniques like Grad-CAM.
o Software Module: XAI Module (part of ACE Software Modules 128).
o Process:
? Generate feature importance scores.
? Create visual heatmaps (e.g., highlighting facial regions or frame segments contributing to a deepfake score).
? Compile per-model sub-scores or anomaly justifications.
o Output: Explainability Data (Heatmaps, Feature Importance, Justifications).
o Arrow to Stage 5.
5. Output Generation Stage (Executed on CPU 122):
• Box 5: "Format & Deliver Results (via Output Generator 140)"
o Input: Final Trust Factor, Final Accuracy Score, Explainability Data.
o Hardware Utilized: CPU (122).
o Software Module: Output formatting component (part of ACE Software Modules 128).
o Process: Structure results into a defined format (e.g., JSON). Prepare for API response or SDK callback.
o Output: Structured Output (Trust Factor, Accuracy, XAI) for client system.
End Block:
• Label: "Results Transmitted to Client Application"

Key Aspects Highlighted by FIG. 2:
• Integration of Hardware: Explicitly mentions CPU and GPU/TPU/NPU utilization at different stages.
• Operational Modules: Clearly defines distinct software modules (Preprocessing, 8 AI Modules, Fusion Engine, XAI).
• Data Processing Steps: Shows the transformation of raw video to normalized frames, then to features, then to modality scores, and finally to a fused trust score.
• Machine Learning Steps: "Machine Learning Inference" is a distinct step for each of the 8 AI modules. The "Trust Fusion Engine" itself can be a machine learning model.
• Flow of Overall Data: Arrows clearly indicate the progression of data and processed information from one module/stage to the next.
• Parallelism: The structure of Stage 2 visually represents the concurrent operation of the eight AI modules.
• End Result Generation: Shows the path to generating the "Trust Factor" and "Explainability Data."
The flowchart of Figure 2 provides a detailed view of the internal workings, schematically indicating how the invented system, Adaptive Cognito Engine, processes data through its various hardware and software components to achieve the desired results. It clarifies the fact that the machine learning steps and data flow critical for the present invention.

For the most preferred exemplary embodiment of the present invention, the schematics of the system construction and the internal operation flow are described as under:-
I. System Construction (Embodiment of the Adaptive Cognito Engine - ACE)
The ACE system is constructed as a computer-implemented invention, typically deployed on an edge computing device, a client's on-premise server infrastructure, or a sufficiently capable end-user device. The primary constructional elements are as follows:
1. Input Acquisition and Interface Module (Ref. FIG. 1, 110):
o Construction: This module comprises physical hardware interfaces (e.g., camera sensor interfaces, network interface cards (NICs), USB ports, file system access controllers) and associated software drivers and APIs.
o Operation: It is configured to receive an input short video segment (e.g., 5-30 seconds in duration) from various sources. The video may be a live stream from an integrated camera, an uploaded video file (e.g., MP4, AVI, WebM formats) received over a network, or a segment from a pre-recorded video. A software controller within this module validates the input (e.g., duration, format, initial integrity check) and buffers the video data for subsequent processing.
2. Preprocessing Module (Ref. FIG. 2, Stage 1; part of ACE Software Modules 128 executed on Processing Unit 120):
o Construction: This software module is executed by the system's Processing Unit (CPU 122, potentially offloading to GPU 124). It utilizes standard and custom image and signal processing libraries (e.g., OpenCV, FFmpeg, Librosa).
o Operation:
? (i) Video De-Multiplexing: Separates the input video segment into its constituent raw video frames and raw audio stream.
? (ii) Frame Processing: For each raw video frame, it performs:
? Frame Extraction: Decodes and sequences frames (e.g., at a target 30fps).
? Resolution Normalization: Resizes frames to a standardized input dimension (e.g., 224x224 pixels) required by subsequent AI modules.
? Lighting Correction: Applies image enhancement techniques, such as histogram equalization, to normalize lighting conditions across frames.
? Color Space Conversion: Converts frames to the appropriate color space (e.g., BGR to RGB) for the AI models.
? (iii) Audio Processing: For the raw audio stream, it performs:
? Format Conversion/Resampling: Ensures audio is in a consistent format and sample rate.
? Feature Priming: May extract initial audio features like Mel-Frequency Cepstral Coefficients (MFCCs) or spectrograms if directly consumed by certain AI modules, or prepares the raw waveform for others.
o Output: A sequence of normalized video frames and a processed audio stream (or features) are passed to the AI Inference Core.
3. AI Inference Core (Ref. FIG. 2, Stage 2; comprising eight ACE AI Modules 128a-h executed on Processing Unit 120, primarily leveraging GPU/TPU/NPU 124):
o Construction: This core consists of a plurality of (e.g., eight) distinct and specialized AI inference software modules. Each module embodies a pre-trained machine learning model (e.g., Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Graph Neural Networks (GNNs), Support Vector Machines (SVMs), statistical models) and associated feature extraction algorithms. These models are stored in Non-Volatile Storage (130) and loaded into Memory (RAM 126) for execution.
o Operation (Concurrent and Independent): The normalized video frames and processed audio features from the Preprocessing Module are fed, as appropriate, to each of the eight AI modules. Each module operates independently and in parallel on the same input segment data:
? (i) Modality-Specific Feature Extraction: Each AI module first extracts features relevant to its designated modality (e.g., visual artifacts by the Deepfake Detection Module, Facial Action Units by the Facial Emotion Module, ocular landmarks and micro-movements by the FETM, rPPG signals by the Heart Rate Module, etc.).
? (ii) Machine Learning Inference: The extracted features are then passed through the respective pre-trained AI model within each module.
? (iii) Generation of Modality-Specific Output: Each module generates an output. This output can be a confidence score (e.g., deepfake likelihood), a classification (e.g., primary emotion), a regression value (e.g., heart rate in BPM), or a set of intermediate feature vectors representing the state of that modality.
o Data Flow: The outputs from all eight AI modules are passed to the Trust Fusion Engine. No inter-module communication or influence occurs during this parallel inference stage.
4. Trust Fusion Engine (Ref. FIG. 2, Stage 3; part of ACE Software Modules 128 executed on CPU 122):
o Construction: This software module implements a statistical aggregation algorithm (e.g., a weighted Bayesian inference model, an ensemble learning classifier like XGBoost, or a custom-defined heuristic model). It includes logic for weighting contributions from different AI modules.
o Operation:
? (i) Input Reception: Receives the modality-specific scores/outputs from all eight AI Inference Modules.
? (ii) Weighting and Aggregation: Applies pre-defined or dynamically adjusted weights to each input score. Dynamic weights may be influenced by factors like input video quality (assessed during preprocessing) or contextual information (if available).
? (iii) Computation of Final Scores: Calculates:
? A Final Trust Factor: A single, holistic score (e.g., scaled 0-10) representing the overall assessed authenticity and trustworthiness of the subject/content in the video.
? A Final Accuracy Score: An internal metric indicating the ACE system's confidence in the generated Trust Factor, potentially based on the congruence and clarity of signals from the constituent modules.
o Output: The Final Trust Factor and Final Accuracy Score are passed for output generation.
5. Explainable AI (XAI) Module (Ref. FIG. 2, Stage 4; part of ACE Software Modules 128, executed on CPU 122 and/or GPU 124):
o Construction: This software module incorporates techniques for model interpretability (e.g., Grad-CAM, SHAP, LIME, or custom attention-map visualization routines for Transformer-based models).
o Operation:
? (i) Input Reception: Receives the Final Trust Factor, intermediate feature vectors, and modality-specific scores from the AI Inference Core and Trust Fusion Engine. It may also receive the normalized video frames.
? (ii) Justification Generation: Generates explainability data, which can include:
? Visual heatmaps overlaid on video frames, indicating regions or temporal segments that most influenced a particular AI module's decision (e.g., highlighting GAN artifacts or specific facial muscle activations for an emotion).
? Feature importance scores indicating which input features or modalities most contributed to the final Trust Factor.
? A breakdown of per-model sub-scores or anomaly justifications.
o Output: Explainability Data is passed for output generation.
6. Output Generator and Interface Module (Ref. FIG. 1, 140):
o Construction: This module comprises software for formatting the final results and hardware interfaces (e.g., NICs) for transmitting the output.
o Operation:
? (i) Result Formatting: Receives the Final Trust Factor, Final Accuracy Score, and Explainability Data. It structures this information into a defined output format (e.g., a JSON object).
? (ii) Output Transmission: Delivers the structured output to the end-user or client application via a secure API (e.g., RESTful API over HTTPS) or an SDK callback. The output is designed for direct consumption by the client system for decision-making (e.g., displaying a trust score, flagging a video for review, automating an action).
7. System Control and Orchestration Layer (Implicit, managed by CPU 122 with OS support):
o Construction: Resides as part of the operating system and ACE's core control logic.
o Operation: Manages the execution flow of all modules, data transfer between modules (primarily in-memory), resource allocation (CPU, GPU, Memory), and error handling. Ensures the stateless processing of video content as described.
II. Method of Operation
The method by which ACE analyzes a deepfake video and confirms its authenticity or lack thereof involves the following steps, executed by the aforementioned system construction:
1. Receiving Input Data (Step S10, FIG. 2 - conceptual step number): The Input Acquisition and Interface Module (110) receives a short video segment.
2. Internal Preprocessing (Step S20): The Preprocessing Module (120) processes the video segment, generating normalized video frames and processed audio features. This data is held temporarily in Memory (126).
3. Parallel Multimodal AI Inference (Step S30): The normalized frames and audio features are concurrently dispatched by the System Control Layer to each of the eight AI Inference Modules (128a-h) within the AI Inference Core. Each module, utilizing primarily GPU/TPU/NPU (124) resources:
o Extracts modality-specific features from the input data.
o Performs machine learning inference using its pre-trained model.
o Generates a modality-specific score or output.
o Example: The FETM module (128c) will analyze ocular dynamics, computing its MOAS. The rPPG module (128g) will attempt to extract a heart rate. The Deepfake Artifact module (128a) will look for visual/frequency anomalies.
4. Trust Factor Computation (Step S40): The Trust Fusion Engine receives all eight modality-specific outputs. It applies its statistical fusion algorithm, considering weights and inter-model congruency, to compute the Final Trust Factor and Final Accuracy Score.
o Reasoning Example: If the Deepfake Artifact module (128a) flags strong visual inconsistencies (low score), the FETM (128c) reports unnatural blink patterns and static pupils (low MOAS), and the rPPG module (128g) fails to detect a physiological heart rate (very low score), these multiple low scores, even if other modules like Speech Sentiment (128e) report neutral/normal, will strongly pull the Final Trust Factor down.
5. Explainability Generation (Step S50, Optional/Configurable): The XAI Module processes the intermediate and final scores to generate justifications.
o Reasoning Example: If the video is flagged as fake, the XAI output might include a heatmap on the face from the Deepfake module highlighting GAN artifacts, a temporal graph from FETM showing abnormal blink intervals, and a note from the rPPG module indicating "No physiological HR detected."
6. Supplying Final Output (Step S60): The Output Generator and Interface Module (140) formats the Final Trust Factor, Accuracy Score, and (if generated) the Explainability Data into a structured response (e.g., JSON) and transmits it to the end-user's application via the secure API.
o Example Output for a Fake Video: {"trust_factor": 2.9, "accuracy_score": 0.95, "is_fake_probability": 0.98, "justification": {"deepfake_artifacts": "High - GAN shimmer detected", "ocular_dynamics": "High - Abnormal blink rate, static pupils", "rppg_hr": "Critical - No physiological signal"}}
o Example Output for a Genuine Video: {"trust_factor": 8.2, "accuracy_score": 0.92, "is_fake_probability": 0.05, "justification": {"all_modalities": "Signals consistent with genuine human behavior"}}
This detailed description of the system's construction and its operational method, including the internal processing mechanisms and data flow, provides a clear understanding for a PHOSITA to implement and utilize the Adaptive Cognito Engine for the stated purpose of deepfake detection and trust verification.

Illustrative case analysis pertaining to performing deepfack video analysis and confirming (with reasoning) whether the said sample video is fake or genuine:
Scenario: E-KYC Video Verification for a High-Value Bank Account Application
A new customer, "Mr. X," is applying for a premium bank account online, which requires a live video KYC (Know Your Customer) interaction. During this 30-second video call, Mr. X is asked to state his name, date of birth, and show his government-issued ID to the camera. The bank has integrated Faceoff's ACE for real-time authenticity verification.
Case 1: Genuine Video
• Mr. X (Genuine): Appears slightly nervous but natural. He speaks clearly, his facial expressions are congruent with his speech, he blinks normally, and his subtle head movements correspond to his focus. His skin tone shows natural color fluctuations.
Case 2: Sophisticated Deepfake Video
• Mr. X (Deepfake): The video is a high-quality deepfake, where an attacker has mapped their own face onto a pre-recorded video of a legitimate-looking individual, or is using a real-time face-swapping filter. The lip movements are well-synced, and the overall facial appearance is convincing at first glance.
The FIG. 3 provides an exemplary conceptual drawing scheme for Deepfake Analysis Workflow & Decision Matrix and based on such conceptual drawing, exemplary scenario of deepfack video authentication and analysis may be understood.
Part A: Flowchart - ACE Processing for E-KYC Video
1. Start: "Live E-KYC Video Stream Input (30s segment of Mr. X)"
2. Box: "Preprocessing (ACE)" (As detailed in FIG. 2: Frame Extraction, Normalization, Audio Processing)
o Output: Normalized Video Frames, Processed Audio
3. Parallel Processing Block (Large box containing 8 smaller boxes): "ACE - 8 Independent AI Modules Analysis"
o Each smaller box represents an AI module (Deepfake Artifacts, Facial Emotion, FETM-Ocular, Posture, Speech Sentiment, Audio Tone, rPPG, SpO2).
o Input to each: Relevant preprocessed data.
o Output from each: Modality-Specific Score/Features.
4. Box: "Trust Fusion Engine (ACE)"
o Input: Outputs from all 8 AI Modules.
o Process: Weighted aggregation, Bayesian inference.
o Output: Overall Trust Factor (0-10), Accuracy Score, XAI Data.
5. Decision Diamond: "Trust Factor >= Bank's Threshold (e.g., 7.0)?"
o Yes -> Box: "Flag as 'Genuine' - Proceed with KYC"
o No -> Box: "Flag as 'Potentially Synthetic/High-Risk' - Escalate for Manual Review / Request Re-Verification"
6. End
Part B: Decision Matrix - Example Output & Reasoning (Table Format)
AI Module (ACE) Observation in Genuine Video (Mr. X) Score (Genuine) Observation in Deepfake Video (Mr. X) Score (Deep-fake) Reasoning for Deepfake Flag
1. Deepfake Artifacts No visual/frequency anomalies 9.5/10 Subtle edge shimmer around jawline, slight blur in T-zone, FFT noise. 3.0/10 GAN artifacts detected spatially and in frequency domain.
2. Facial Emotion Natural, congruent expressions 8.0/10 Micro-expressions slightly stiff, AU6+AU12 (smile) onset too uniform. 4.5/10 Unnatural/robotic onset of smile (AU6+AU12). Lack of subtle secondary AU activation (e.g., AU1 for inner brow with genuine surprise if expressed).
3. FETM (Ocular Dynamics) Normal blink rate, saccades, pupil 8.5/10 Blink rate slightly too low (e.g., 1 blink in 10s), pupil static. 2.5/10 Abnormal blink pattern (too infrequent). BIS shows blink not aligned with speech emphasis. DPOM reveals lack of pupil micro-oscillations.
4. Posture Analysis Natural, slight, smooth movements 7.5/10 Head very still, slight "puppet-like" micro-jitters in neck rotation. 4.0/10 Unnatural stillness or micro-jitters inconsistent with human movement.
5. Speech Sentiment Clear, consistent sentiment 8.0/10 Sentiment mostly neutral, but some words have odd flat delivery. 6.0/10 While text is okay, the delivery (prosody) is being picked up by Audio Tone.
6. Audio Tone Natural prosody, slight nervousness 7.0/10 Voice slightly monotonous, lacks expected pitch variation for engagement. 3.5/10 Unnatural prosody, flat intonation despite context. Possible audio synthesis or poor voice-over.
7. rPPG (Heart Rate) HR ~85bpm, natural variability 7.5/10 No detectable rPPG signal or flatline (synthetic skin texture). 1.0/10 Lack of physiological blood flow signal (rPPG) from facial skin pixels.
8. SpO2 (Oxygen Saturation) SpO2 ~98%, stable 8.0/10 No reliable SpO2 estimation possible (due to rPPG failure). 1.5/10 Absence of correlated physiological signals (SpO2 typically derived from rPPG analysis).
OVERALL (ACE Fusion Engine) Trust: 8.2/10 Trust: 2.9/10 Multiple biometric and behavioral inconsistencies across visual and physiological modalities strongly indicate synthetic generation despite good lip-sync.
Confirmation & Reasoning by ACE (Adaptive Cognito Engine):
Case 1: Genuine Video (Mr. X)
• Deepfake Artifacts Module: Detects no significant spatial inconsistencies (e.g., edge blending errors, texture anomalies) or temporal flickers. Frequency domain analysis shows natural noise patterns. Score: High (e.g., 9.5/10).
• Facial Emotion Module: Recognizes expressions (e.g., slight nervousness, polite smile) as genuine and congruent with the context of a KYC call. Action Unit (AU) activations follow natural onset-offset dynamics. Score: High (e.g., 8.0/10).
• FETM (Ocular Dynamics Module): Observes natural blink rates (e.g., every 4-6 seconds), smooth saccadic movements as Mr. X looks at the camera/ID, and appropriate pupil dilation changes in response to slight head movements or ambient light. No BOCE or BIS anomalies. Score: High (e.g., 8.5/10).
• Posture Analysis Module: Detects natural, subtle shifts in posture and head orientation; no unnatural stillness or robotic movements. Score: Moderately High (e.g., 7.5/10).
• Speech Sentiment Module: Analyzes spoken words ("My name is...", "My date of birth is...") and finds the sentiment to be neutral and appropriate. Score: High (e.g., 8.0/10).
• Audio Tone Module: Detects natural intonation and prosody, perhaps with slight stress indicators common in such interactions. No signs of synthetic voice. Score: Moderately High (e.g., 7.0/10).
• rPPG Module: Successfully extracts a plausible heart rate (e.g., 85 bpm) with natural variability from skin pixel fluctuations. Score: Moderately High (e.g., 7.5/10).
• SpO2 Module: Estimates a normal SpO2 level (e.g., 98%) based on the rPPG signal analysis. Score: High (e.g., 8.0/10).
• ACE Trust Fusion Engine: All modules report high confidence in genuineness. The weighted aggregation results in a High Overall Trust Factor (e.g., 8.2/10).
• Confirmation: GENUINE.
• Reasoning (XAI Output): "Consistent positive signals across all 8 biometric and behavioral modalities. Natural ocular dynamics, congruent facial expressions, and detectable physiological signs (HR, SpO2) support authenticity. Minor nervousness indicators are within normal human range for this context."
Case 2: Sophisticated Deepfake Video (Mr. X)
• Deepfake Artifacts Module: While the face swap is high quality, this module might detect subtle edge blending inconsistencies around the jawline or forehead that don't perfectly match ambient lighting, or minor texture anomalies in the T-zone not present in genuine skin. Frequency analysis might reveal unnatural periodicity or lack of high-frequency detail characteristic of GANs. Score: Low (e.g., 3.0/10).
• Facial Emotion Module: The deepfake might replicate basic expressions, but micro-expressions are often absent or appear stiff. The onset and offset of a smile (e.g., AU12) might be too uniform or lack co-activation with AU6 (cheek raiser) characteristic of a Duchenne smile. Score: Low-Medium (e.g., 4.5/10).
• FETM (Ocular Dynamics Module): This is often a critical point of failure for deepfakes.
o Blink Kinematics: The deepfake might have an unnaturally low or high blink rate, or blinks might be perfectly synchronized between both eyes (uncommon in humans). The Blink-Incongruence Score (BIS) might flag blinks as non-correlating with speech emphasis or emotional cues from other modalities.
o Pupil Dynamics (NORS/DPOM): Deepfakes often fail to replicate realistic pupil responses to light or cognitive load; pupils might appear static or dilate/constrict unnaturally.
o Saccadic Integrity & BOCE: Gaze might appear "locked on" or exhibit unnatural, non-physiological saccadic jumps. The Bilateral Ocular Congruence Engine (BOCE) may detect divergence.
o Score: Very Low (e.g., 2.5/10).
• Posture Analysis Module: The underlying body in the pre-recorded video might be genuine, but if the head is purely synthetic or poorly tracked, there might be a disconnect ("puppet-like" effect) or unnatural stillness of the head relative to subtle (genuine) body movements. Score: Medium (e.g., 4.0/10 if body is real, lower if head motion is also synthetic).
• Speech Sentiment Module: If the audio is also synthetic (voice clone), sentiment analysis might be reasonable if the text is coherent. Score: Medium (e.g., 6.0/10).
• Audio Tone Module: Synthesized voices often lack the subtle pitch variations, prosodic richness, and breathing sounds of genuine speech. Spectrograms might show unnatural harmonics or lack of micro-tremors. Score: Low (e.g., 3.5/10 if voice is cloned).
• rPPG Module: This is a very strong indicator. Synthetically generated skin textures typically lack the subtle, time-varying sub-dermal blood flow information necessary to generate a rPPG signal. The module will likely fail to detect a coherent heart rate. Score: Very Low (e.g., 1.0/10).
• SpO2 Module: As SpO2 is derived from rPPG in Faceoff, this will also likely fail or produce highly unreliable/inconsistent readings. Score: Very Low (e.g., 1.5/10).
• ACE Trust Fusion Engine: Multiple critical modules (FETM, rPPG, SpO2, Deepfake Artifacts, Audio Tone) report strong indicators of synthetic generation or lack of liveness. The weighted aggregation results in a Very Low Overall Trust Factor (e.g., 2.9/10).
• Confirmation: FAKE/SYNTHETIC.
• Reasoning (XAI Output): "Multiple critical biometric failures: No detectable physiological heart rate (rPPG) or SpO2 signals. Ocular dynamics exhibit unnatural blink patterns and static pupil response inconsistent with liveness (FETM flags). Subtle visual artifacts detected around facial edges. Audio tone suggests potential synthesis. High probability of synthetic media despite superficially convincing visual appearance and lip-sync."
This example illustrates how ACE leverages its 8 independent modules to perform a comprehensive analysis. The strength of the system lies in its ability to find inconsistencies across modalities, even if some individual aspects of the deepfake are well-executed. The failure to replicate complex, correlated human biometric and behavioral signals is where deepfakes are typically exposed by ACE.
It is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims. It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. All combinations of the embodiments pertaining to the invention are specifically embraced by the present invention and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all sub-combinations of the various embodiments and elements thereof are also specifically embraced by the present invention and are disclosed herein just as if each and every such sub-combination was individually and explicitly disclosed herein.

==================
GLOSSARY OF TERMS / ACRONYMS
• ACE: Adaptive Cognito Engine (The core inventive AI framework of the present invention)
• AI: Artificial Intelligence
• API: Application Programming Interface (A software intermediary that allows two applications to talk to each other)
• AU: Action Unit (Describing specific facial muscle movements, commonly used in FACS)
• BGR: Blue Green Red (A color space commonly used in digital imaging, e.g., by OpenCV)
• BIS: Blink-Incongruence Score (A novel metric proposed by FETM)
• BOCE: Bilateral Ocular Congruence Engine (A novel component proposed by FETM)
• BPM: Beats Per Minute (Unit for heart rate)
• CNN: Convolutional Neural Network (A class of deep neural networks, most commonly applied to analyzing visual imagery)
• CPU: Central Processing Unit (The primary component of a computer that executes instructions)
• CRI: Computer-Related Invention
• CSI-2 (MIPI CSI-2): Camera Serial Interface 2 (A specification of the Mobile Industry Processor Interface (MIPI) Alliance, commonly used for camera connections in mobile devices)
• DCT: Discrete Cosine Transform (A mathematical transformation used in signal and image processing, notably in JPEG compression)
• DFDC: Deepfake Detection Challenge (A known public dataset for deepfake research)
• DFT: Discrete Fourier Transform (A mathematical transformation used in signal processing to convert a signal into its frequency components)
• DPDP Act: Digital Personal Data Protection Act (Referring to data privacy legislation, e.g., India's DPDP Act 2023)
• DPOM: Differential Pupil Oscillation Mapping (A novel technique proposed by FETM)
• EAR: Eye Aspect Ratio (A common metric used in eye tracking for blink detection)
• FACS: Facial Action Coding System (A comprehensive, anatomically based system for describing all observable facial movement)
• FETM: Faceoff Eye Tracking Module (A specialized ocular dynamics analysis module within ACE)
• FFT: Fast Fourier Transform (An algorithm to compute the DFT efficiently)
• FFmpeg: (A free and open-source software project consisting of a suite of libraries and programs for handling video, audio, and other multimedia files and streams. Used here as a generic example of a video processing library.)
• FIG.: Figure (Used to refer to drawings in a patent application)
• FOAI: Fraud-Oriented AI (An alternative name used for the ACE system, emphasizing its application)
• GAN: Generative Adversarial Network (A class of machine learning frameworks)
• GDPR: General Data Protection Regulation (A regulation in EU law on data protection and privacy)
• GJE: Gaze Jitter Entropy (A metric related to gaze stability)
• GFD: Gaze Fixation Density (A metric related to gaze patterns)
• GDV: Gaze Divergence Vector (A metric related to the difference in gaze direction between two eyes)
• GOA: Grasshopper Optimization Algorithm (A nature-inspired metaheuristic algorithm)
• GPU: Graphics Processing Unit (A specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device; widely used for parallel processing in AI)
• GRU: Gated Recurrent Unit (A type of recurrent neural network)
• HIPAA: Health Insurance Portability and Accountability Act (A US federal law protecting sensitive patient health information)
• HR: Heart Rate
• JSON: JavaScript Object Notation (A lightweight data-interchange format)
• KYC: Know Your Customer (Verification processes used by businesses)
• Librosa: (A python package for music and audio analysis. Used here as a generic example of an audio processing library.)
• LIME: Local Interpretable Model-agnostic Explanations (A technique for explaining predictions of machine learning models)
• LSTM: Long Short-Term Memory (A type of recurrent neural network)
• MFCC: Mel-Frequency Cepstral Coefficients (Features widely used in automatic speech and speaker recognition)
• MIPI: Mobile Industry Processor Interface (An alliance that develops interface specifications for mobile and mobile-influenced applications)
• MOAS: Multimodal Ocular Anomaly Score (A novel fused score from FETM)
• MP4, AVI, WebM: Common digital video container formats.
• N-module: Referring to a system with 'N' number of distinct modules.
• NIC: Network Interface Card (A hardware component that connects a computer to a computer network)
• NIO: Nature-Inspired Optimization
• NLP: Natural Language Processing (A subfield of AI concerned with the interaction between computers and humans in natural language)
• NORS: Neuro-Ocular Reflex Simulator (A novel component proposed by FETM)
• NPU: Neural Processing Unit (A specialized processor or circuit designed to accelerate machine learning algorithms, particularly artificial neural networks)
• NVMe: Non-Volatile Memory Express (A specification for accessing SSDs attached through the PCI Express bus)
• OpenCV: Open Source Computer Vision Library (A library of programming functions mainly aimed at real-time computer vision)
• ONNX: Open Neural Network Exchange (An open format built to represent machine learning models)
• OS: Operating System
• PHOSITA: Person Having Ordinary Skill InThe Art (A legal standard in patent law)
• PII: Personally Identifiable Information
• PIS: Pupil Inertia Score (A metric proposed by FETM)
• PSO: Particle Swarm Optimization (A nature-inspired metaheuristic algorithm)
• RAM: Random Access Memory
• REST API: Representational State Transfer Application Programming Interface
• RGB: Red Green Blue (An additive color model in which red, green, and blue primary colors of light are added together in various ways to reproduce a broad array of colors)
• RNN: Recurrent Neural Network
• rPPG: remote Photoplethysmography (A technique for contactless monitoring of heart rate)
• SDK: Software Development Kit
• SHAP: SHapley Additive exPlanations (A game theoretic approach to explain the output of any machine learning model)
• SpO2: Peripheral Capillary Oxygen Saturation (An estimation of the oxygen saturation level in blood)
• SSD: Solid-State Drive
• SVM: Support Vector Machine (A type of supervised machine learning algorithm)
• TAW: Temporal Attention Windows (A concept associated with the Microsaccade Transformer Engine in FETM)
• TPM: Trusted Platform Module (A dedicated microcontroller designed to secure hardware through integrated cryptographic keys)
• TPU: Tensor Processing Unit (An AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning)
• U-Net: (A convolutional neural network architecture for fast and precise segmentation of images)
• USB: Universal Serial Bus
• XAI: Explainable Artificial Intelligence
• XGBoost: Extreme Gradient Boosting (An open-source software library which provides a regularizing gradient boosting framework)
• YOLOv8m: You Only Look Once version 8 medium (A real-time object detection system. The "m" denotes a medium-sized variant.)
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• "Faceoff", "FOAI", "ACE": These are defined as the names of the invention/engine itself. This is acceptable.
• "FOAI Box": This is a conceptual name for an appliance embodying the invention. Acceptable.
• "YOLOv8m", "MediaPipe Iris", "OpenPose", "ResNet-50", "Wav2Vec2", "BERT", "XGBoost", "XceptionNet", "MesoNet", "DeepRhythm", "FakeCatcher", "TensorRT", "CoreML", "SQLite", "FastAPI", "Flask", "Uvicorn", "Gunicorn", "Docker", "Kubernetes", "SlowAPI", "Prometheus", "Grafana", "Captum", "PyTorch Lightning", "Flower", "FedML", "TorchServe", "Ray", "Locust", "Artillery", "SELinux", "AppArmor", "UFW", "Keygen.sh", "Mender", "Balena Cloud":
o Action: These are names of specific open-source libraries, algorithms, datasets, or commercial products/services. In a patent, it's generally better to describe the functionality or type of component rather than relying solely on a specific brand name, unless that brand name is essential to define a standard or a specific embodiment that cannot be otherwise described.
o Generic Alternatives/Elaboration:
? For YOLOv8m/MediaPipe Iris/OpenPose/ResNet-50/Wav2Vec2/BERT/XceptionNet/MesoNet: Describe as "a real-time object detection model," "a facial landmark tracking framework," "a human pose estimation library," "a pre-trained convolutional neural network architecture for image feature extraction," "a pre-trained self-supervised learning model for speech feature extraction," "a transformer-based language model for contextual embeddings," "a specific type of convolutional neural network architecture," respectively. Then, one can state: "In an exemplary embodiment, [BrandName] may be utilized."
? For TensorRT/CoreML: "model optimization and inference acceleration frameworks for deployment on [NVIDIA GPUs/Apple devices, respectively]."
? For SQLite: "a lightweight local relational database management system."
? For FastAPI/Flask/Uvicorn/Gunicorn: "a web framework for building APIs," "an ASGI server," "a Python WSGI HTTP server."
? For Docker/Kubernetes: "containerization technology," "a container orchestration platform."
? For SlowAPI: "a rate-limiting library for web frameworks."
? For Prometheus/Grafana: "a monitoring and alerting toolkit," "an analytics and interactive visualization web application."
? For Captum: "a model interpretability library for [framework, e.g., PyTorch]."
? For PyTorch Lightning/Flower/FedML: "a high-level interface for [PyTorch training/federated learning]."
? For TorchServe/Ray Serve: "a model serving framework for [PyTorch/general applications]."
? For Keygen.sh/Mender/Balena Cloud: "a third-party license management service," "an over-the-air (OTA) software update management platform."
• Operating Systems (e.g., "Hardened Linux", "Android", "iOS"): Generally acceptable as they define broad platforms. "Hardened Linux" implies specific security configurations.
• Cloud Providers (e.g., "AWS", "Azure", "GCP"): When discussing client-controlled infrastructure, naming these is acceptable as examples of where the client might deploy ACE. Faceoff itself is not tied to them.
• Hardware (e.g., "NVIDIA Jetson AGX Orin", "Intel RealSense D435", "Logitech Brio 4K", "Dell PowerEdge R650"): Acceptable as specific examples of hardware components that can be used to construct or run the system. It's good to also describe their generic function (e.g., "an edge AI computing device," "a depth-sensing camera," "a high-resolution webcam," "an enterprise-grade server").

,CLAIMS:We Claim:
1. A system as Adaptive Cognito Engine (ACE), also referred to as "Faceoff AI" or "FOAI" and method for detecting synthetic video-based fraud utilizing a plurality of independently operating modality-specific machine learning models that execute on local or edge computing infrastructure; wherein said system architecture comprises Input Acquisition and Interface Module (110), Processing Unit (120), Non-Volatile Storage (130), Output Generator/Interface (140) and Secure Enclave / TPM (Trusted Platform Module) (150) and the said Adaptive Cgnito Engine (ACE) is characterized in that:
• a decentralized, privacy-focused AI system capable of evaluating short video content, preferably a 5 to 30 second video;
• using multiple independently operating AI models that assess different modalities;
• each model produces a score reflecting visual, auditory, or biometric authenticity;
• these scores are fused using a statistical engine to compute a final Trust Factor and Accuracy Score;
• a computer-implemented system, typically deployed on an edge computing device, a client's on-premise server infrastructure, or a sufficiently capable end-user device; and
• the system functions without storing video content or transmitting personal data, ensuring full compliance with GDPR, HIPAA, and CCPA.
2. The system as the Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein the preferred embodiment of said system comprises eight modules, namely:
i. A Deepfake Artifact Detection Module for analyzing visual and frequency-domain inconsistencies indicative of synthetic generation;
ii. A Facial Emotion Analysis Module for recognizing and assessing the congruence of facial expressions;
iii. An Ocular Dynamics Analysis Module (FETM), a specialized component of ACE, for granularly analyzing advanced eye-tracking biometrics, blink kinematics, pupil reflexes, and micro-expressions around the ocular region;
iv. A Posture and Gesture Analysis Module for interpreting body language and motion dynamics;
v. A Speech Sentiment Analysis Module for understanding the emotional content of spoken language using Natural Language Processing;
vi. An Audio Tone and Prosody Analysis Module for evaluating vocal characteristics indicative of stress or deception;
vii. A Remote Photoplethysmography (rPPG) Module for contactless estimation of physiological signals such as heart rate variability; and
viii. A Blood Oxygen Saturation (SpO2) Estimation Module for contactless assessment of another key physiological indicator;
3. The system as the Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein Input Acquisition and Interface Module (110) is responsible for receiving the input short video segment (112), for example of 5-30 seconds, and it comprises:-
o Hardware: This includes standard camera interfaces (e.g., USB, MIPI CSI-2) if integrated directly with a capture device, network interface cards (NICs) (e.g., Ethernet, Wi-Fi) for receiving video data from a network or an application or file system interfaces for accessing stored video files; and
o Software Controller: A software module, part of ACE, manages the input stream, ensuring the video data is correctly formatted and buffered for processing It validates input parameters (e.g., video length, resolution, supported codecs like MP4, AVI, WebM);
And said Input Acquisition and Interface Module (110) is characterized in the functionality as acquiring the video data and passing it to the Preprocessing Unit (120).
4. The system as the Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein the Processing Unit (120) (Implementing the Adaptive Cognito Engine - ACE is the core of the said system, typically comprising one or more processors and specialized hardware accelerators, namely as under:
o Central Processing Unit(s) (CPU) (122): Standard multi-core CPUs (e.g., ARM64, Intel Xeon, AMD Ryzen) manage the overall workflow orchestration of ACE, handle certain preprocessing tasks, and may execute some AI model inference if specialized accelerators are unavailable or not optimal for a given module. The CPU also executes the final Trust Fusion Engine logic;
o Graphics Processing Unit(s) (GPU) / Tensor Processing Unit(s) (TPU) / Neural Processing Unit(s) (NPU) (124): Specialized hardware accelerators (e.g., NVIDIA GPUs like RTX series or Jetson AGX Orin, Google TPUs, or mobile NPUs) are utilized for computationally intensive tasks, primarily the inference operations of the eight (8) distinct AI modules within ACE, wherein ACE is designed to leverage these accelerators for real-time performance (e.g., <50ms per module for deepfake detection, <100ms for multi-model analysis);
o Memory (RAM) (126): Sufficient Random Access Memory (e.g., 64-128 GB on an appliance, or system-dependent RAM on client infrastructure/devices) is required for holding the AI models, intermediate feature vectors, and buffered video frames during the stateless processing, wherein ACE processes video in-memory to enhance speed and privacy, discarding frame data immediately after the relevant features are extracted and processed by the AI modules;
and
o ACE Software Modules (128) (Stored in Non-Volatile Storage and loaded into RAM):
? Preprocessing Module: Software for frame extraction (e.g., using OpenCV, FFmpeg), audio separation (e.g., using Librosa), resolution normalization, and lighting correction (e.g., histogram equalization);
? Eight Independent AI Inference Modules (128a-h): These are the core Deepfake Detection, Facial Emotion, FETM (Ocular Dynamics), Posture, Speech Sentiment, Audio Tone, rPPG, and SpO2 modules wherein each module is a distinct software component, potentially comprising pre-trained neural network models (e.g., in ONNX, TensorRT, or PyTorch/Tensor Flow format), statistical models, and associated feature extraction algorithms, and wherein these models are loaded into the memory (126) and executed on the appropriate processing hardware (122 or 124);
? Trust Fusion Engine Module: Software implementing the statistical aggregation algorithm (e.g., weighted Bayesian inference, ensemble learning) to compute the final Trust Factor and Accuracy Score from the outputs of modules (128a-h);
? XAI (Explainable AI) Module: Software components for generating justifications (heatmaps, feature importance scores) associated with the Trust Factor.
5. The system as the Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein Non-Volatile Storage (130) comprises:-
o Hardware: Solid-State Drives (SSD, e.g., NVMe SSD of 2TB on an appliance) or other persistent storage;
o Content: Stores the operating system (e.g., hardened Linux), the ACE software including all AI models and the fusion engine, configuration files, and (optionally and securely, if configured by the client) anonymized API usage logs or XAI outputs for audit purposes, wherein crucially, raw input video data or identifiable biometric features are not persistently stored by ACE by default after processing;
o Database Controller: While ACE itself doesn't maintain a large user database of raw videos, it might interact with a minimal local configuration database (e.g., SQLite) for system settings or license management and if deployed in an enterprise, it may log anonymized statistical usage to a secure, designated database controlled by the client or Faceoff for billing/licensing, ensuring no PII is transmitted.
6. The system as the Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein Output Generator/Interface (140) is responsible for providing the results of the ACE analysis and said Output Generator/Interface (140) includes:
o Hardware: NICs for network transmission, or display interfaces if directly outputting to a screen;
o Software Controller: An ACE software module formats the output, which includes the Trust Factor, Accuracy Score, and optionally, XAI data (justifications, heatmaps);
o Format: Outputs are typically provided in a structured format like JSON via a secure REST API or SDK integrated into the client's application or system; and
o Actionable Output: The output is designed to be directly usable by the client system for decision-making (e.g., flagging content, alerting an operator, denying access, or approving a transaction).
7. The system as the Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein Secure Enclave/TPM (Trusted Platform Module) (150) (Optional but preferred for high-security deployments, e.g., FOAI Box) comprises specialized secure co-processor (e.g., ARM TrustZone, Intel SGX) or a TPM chip and wherein said Secure Enclave/TPM (Trusted Platform Module) (150) is characterized in its functionality as protecting cryptographic keys, model parameters (if encrypted at rest on storage 130), secure boot processes, and ensures the integrity of the inference logs and XAI outputs, preventing tampering.
8. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claim 1, includes the following operational data-flow:-
i. An Input Video Segment (112) is received by the Input Interface/Receiver (110) of the system (100 or 102 or 104);
ii. The video segment (112) is passed to the Preprocessing Module within the Processing Unit (120);
iii. Preprocessed data (frames, audio streams) are concurrently fed to the Eight Independent AI Inference Modules (128a-h) executing on appropriate hardware (122, 124);
iv. Each AI module outputs modality-specific scores/features to the Trust Fusion Engine Module;
v. The Trust Fusion Engine computes the final Trust Factor and Accuracy Score;
vi. The XAI Module (if activated) generates justifications;
vii. The Output Generator/Interface (140) formats and delivers these results (e.g., via API) to the requesting client application or system;
viii. During this process, temporary data resides in Memory (126) and is discarded post-processing, ensuring stateless operation. Models and system software are loaded from Non-Volatile Storage (130);
and
ix. Anonymized API call metadata (e.g., a counter for number of videos processed, associated API key, timestamp, but not the video itself or its content) might be logged to a designated secure location or transmitted for licensing and operational analytics, ensuring privacy of the core video data.
9. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claim 1, wherein the Input Acquisition and Interface Module (110) is configured to receive an input short video segment (e.g., 5-30 seconds in duration) from various sources, wherein the video is a live stream from an integrated camera, an uploaded video file (e.g., MP4, AVI, WebM formats) received over a network, or a segment from a pre-recorded video and wherein a software controller within this module (110) validates the input (e.g., duration, format, initial integrity check) and buffers the video-data for subsequent processing.
10. A method for synthetic media fraud detection, comprising:
(a) receiving, at a local computing environment, a video of a predetermined duration;
(b) preprocessing said video to extract frame sequences, audio tracks, and biometric indicators;
(c) analyzing extracted data via a plurality of independently executing AI models to produce modality-specific anomaly scores;
(d) aggregating said scores using a statistical inference algorithm to generate a Trust Factor and Accuracy Score;
and
(e) outputting said scores without persisting, transmitting, or exposing the input video beyond the local environment.
11. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 1 and 10, wherein the method by which the system ACE analyzes a deepfake video and confirms its authenticity or lack thereof involves the following steps:
• Receiving Input Data (Step S10, FIG. 2 - conceptual step number): The Input Acquisition and Interface Module (110) receives a short video segment;
• Internal Preprocessing (Step S20): The Preprocessing Module (120) processes the video segment, generating normalized video frames and processed audio features, wherein this data is held temporarily in Memory (126);
• Parallel Multimodal AI Inference (Step S30): The normalized frames and audio features are concurrently dispatched by the System Control Layer to each of the eight AI Inference Modules (128a-h) within the AI Inference Core, wherein each module, utilizing primarily GPU/TPU/NPU (124) resources:
o Extracts modality-specific features from the input data;
o Performs machine learning inference using its pre-trained model; and
o Generates a modality-specific score or output;
• Trust Factor Computation (Step S40): The Trust Fusion Engine receives all eight modality-specific outputs. It applies its statistical fusion algorithm, considering weights and inter-model congruency, to compute the Final Trust Factor and Final Accuracy Score;
• Explainability Generation (Step S50, Optional/Configurable): The XAI Module processes the intermediate and final scores to generate justifications;
and
• Supplying Final Output (Step S60): The Output Generator and Interface Module (140) formats the Final Trust Factor, Accuracy Score, and (if generated) the Explainability Data into a structured response (e.g., JSON) and transmits it to the end-user's application via the secure API.
12. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 11, wherein said modality-specific AI models comprise models for at least one of facial microexpression analysis, eye tracking, posture estimation, voice sentiment classification, tone modulation detection, and non-contact biometric estimation of heart rate and oxygen saturation.
13. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 12, wherein said statistical inference algorithm comprises a weighted Bayesian fusion model calibrated to prioritize biometric and visual anomalies over audio or posture-based discrepancies.
14. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 13, further comprising operating said models within a stateless containerized execution environment that ensures no caching or retention of user video or metadata.
15. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein said system is deployed within an edge-computing device comprising at least one AI accelerator, and wherein said video is processed in under five seconds with no external cloud connectivity.
16. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein the Pre-processing module, part of ACE Software Modules (128) executed on Processing Unit (120), is provisioned for the followings:-
o Operation:
? (i) Video De-Multiplexing: Separates the input video segment into its constituent raw video frames and raw audio stream;
? (ii) Frame Processing: For each raw video frame, it performs:
? Frame Extraction: Decodes and sequences frames (e.g., at a target 30fps);
? Resolution Normalization: Resizes frames to a standardized input dimension (e.g., 224x224 pixels) required by subsequent AI modules;
? Lighting Correction: Applies image enhancement techniques, such as histogram equalization, to normalize lighting conditions across frames;
? Color Space Conversion: Converts frames to the appropriate color space (e.g., BGR to RGB) for the AI models;
? (iii) Audio Processing: For the raw audio stream, it performs:
? Format Conversion/Resampling: Ensures audio is in a consistent format and sample rate;
? Feature Priming: May extract initial audio features like Mel-Frequency Cepstral Coefficients (MFCCs) or spectrograms if directly consumed by certain AI modules, or prepares the raw waveform for others;
And
o Output: A sequence of normalized video frames and a processed audio stream (or features) are passed to the AI Inference Core.

17. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein the AI Inference Core, comprising eight ACE AI Modules (128a-h) executed on Processing Unit (120), primarily leveraging GPU/TPU/NPU (124), is provisioned for the followings:-
o Operation (Concurrent and Independent): The normalized video frames and processed audio features from the Preprocessing Module are fed, as appropriate, to each of the eight AI modules, wherein each module operates independently and in parallel on the same input segment data:
? (i) Modality-Specific Feature Extraction: Each AI module first extracts features relevant to its designated modality (e.g., visual artifacts by the Deepfake Detection Module, Facial Action Units by the Facial Emotion Module, ocular landmarks and micro-movements by the FETM, rPPG signals by the Heart Rate Module, etc.);
? (ii) Machine Learning Inference: The extracted features are then passed through the respective pre-trained AI model within each module;
? (iii) Generation of Modality-Specific Output: Each module generates an output. This output can be a confidence score (e.g., deepfake likelihood), a classification (e.g., primary emotion), a regression value (e.g., heart rate in BPM), or a set of intermediate feature vectors representing the state of that modality;
And
o Data Flow: The outputs from all eight AI modules are passed to the Trust Fusion Engine, wherein no inter-module communication or influence occurs during this parallel inference stage.
18. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein the Trust Fusion Engine, part of ACE Software Modules (128) executed on CPU (122), is provisioned for the followings:-
o Operation:
? (i) Input Reception: Receives the modality-specific scores/outputs from all eight AI Inference Modules;
? (ii) Weighting and Aggregation: Applies pre-defined or dynamically adjusted weights to each input score. Dynamic weights may be influenced by factors like input video quality (assessed during preprocessing) or contextual information (if available);
? (iii) Computation of Final Scores: Calculates:
? A Final Trust Factor: A single, holistic score (e.g., scaled 0-10) representing the overall assessed authenticity and trustworthiness of the subject/content in the video;
? A Final Accuracy Score: An internal metric indicating the ACE system's confidence in the generated Trust Factor, potentially based on the congruence and clarity of signals from the constituent modules;
and
o Output: The Final Trust Factor and Final Accuracy Score are passed for output generation.
19. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein the Explainable AI (XAI) Module, part of ACE Software Modules (128) executed on CPU (122) and/or GPU (124), is provisioned for the followings:-
o Operation:
? (i) Input Reception: Receives the Final Trust Factor, intermediate feature vectors, and modality-specific scores from the AI Inference Core and Trust Fusion Engine. It may also receive the normalized video frames;
? (ii) Justification Generation: Generates explainability data, which can include:
? Visual heatmaps overlaid on video frames, indicating regions or temporal segments that most influenced a particular AI module's decision (e.g., highlighting GAN artifacts or specific facial muscle activations for an emotion);
? Feature importance scores indicating which input features or modalities most contributed to the final Trust Factor;
? A breakdown of per-model sub-scores or anomaly justifications;
and
o Output: Explainability Data is passed for output generation.
20. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein Output Generator and Interface Module (140) is provisioned for the followings:-
o Operation:
? (i) Result Formatting: Receives the Final Trust Factor, Final Accuracy Score, and Explainability Data. It structures this information into a defined output format (e.g., a JSON object); and
? (ii) Output Transmission: Delivers the structured output to the end-user or client application via a secure API (e.g., RESTful API over HTTPS) or an SDK callback. The output is designed for direct consumption by the client system for decision-making (e.g., displaying a trust score, flagging a video for review, automating an action).
21. The method of operating the said system Adaptive Cognito Engine (ACE), as claimed in claims 9 to 14, wherein the System Control and Orchestration Layer (Implicit, managed by CPU (122) with OS support), is provisioned for the followings:-
o Operation: Manages the execution flow of all modules, data transfer between modules (primarily in-memory), resource allocation (CPU, GPU, Memory), and error handling and it also ensures the stateless processing of video content as described.