1. A novel adversarial defense framework that utilizes frequency-aware adversarial training to detect and mitigate high-frequency perturbations, improving the robustness of deep learning-based computer vision models.

2. An adaptive defense mechanism based on reinforcement learning that dynamically adjusts model parameters in real-time to counter adversarial attacks.

3. A multi-scale feature alignment module that enhances feature representation at different abstraction levels, reducing the impact of adversarial noise while maintaining accuracy on clean samples.

Description:Title:

Robust Adversarial Network for Enhancing Computer Vision Models Against Perturbations

Field of the Invention

[0001] The present invention is related to the computer science and machine learning field.
Background

[0002] Deep learning-based computer vision models are highly sensitive to adversarial perturbations, where small, imperceptible modifications to input images can significantly degrade model performance. These vulnerabilities pose serious risks in critical applications such as autonomous driving, healthcare, and security surveillance.
[0003] Traditional adversarial training methods often rely on generating perturbed samples and retraining models on them. However, these approaches require extensive computational resources, struggle to generalize across different attack types, and may lead to reduced model accuracy on clean data.
[0004] Recent studies indicate that adversarial perturbations often exist in specific frequency bands. However, existing defense mechanisms do not effectively utilize frequency-aware strategies to detect and mitigate such perturbations, leading to inefficient adversarial robustness.
[0005] As AI-powered vision systems become integral to industries like finance, defense, and healthcare, the demand for robust and secure AI models has increased. A novel adversarial network that enhances robustness while maintaining computational efficiency is crucial for real-world deployment.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[0011] To develop a Robust Adversarial Network (RAN) that enhances the resilience of computer vision models against adversarial attacks using frequency-aware training and adaptive defenses.
[0012]. To integrate multi-scale feature alignment techniques that improve model robustness at different levels of abstraction without sacrificing accuracy on clean data.

Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype.

Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.
[0015] Gather diverse datasets containing both clean and adversarial perturbed images. Apply data augmentation techniques to improve generalization.
[0016] Study various attack methods (e.g., FGSM, PGD, CW) to identify their impact on deep learning models. Perform frequency domain analysis to understand perturbation characteristics.
[0017] Develop a model incorporating frequency-aware adversarial training, multi-scale feature alignment, and adaptive defense mechanisms.
[0018] Implement Fourier-based filtering techniques to identify adversarial patterns. Train models using perturbation-resistant loss functions.
[0019] Use multi-resolution analysis and attention-based mechanisms to align features across different layers of the model.
[0020] Employ reinforcement learning-based parameter tuning to adjust model behavior dynamically in response to detected adversarial threats.
[0021] Compare model performance against standard adversarial training methods using accuracy, robustness, and computational efficiency metrics.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:We Claim:

1. A novel adversarial defense framework that utilizes frequency-aware adversarial training to detect and mitigate high-frequency perturbations, improving the robustness of deep learning-based computer vision models.
2. An adaptive defense mechanism based on reinforcement learning that dynamically adjusts model parameters in real-time to counter adversarial attacks.
3. A multi-scale feature alignment module that enhances feature representation at different abstraction levels, reducing the impact of adversarial noise while maintaining accuracy on clean samples.