Description:Field of the Invention

Generally, the present disclosure relates to systems for detecting forgery in text. Particularly, the present disclosure relates to a system for artificial intelligence (AI) text forgery detection using blockchain verification.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Given the rapid advancement and widespread application of artificial intelligence (AI) in generating textual content, concerns over the authenticity and integrity of such content have become increasingly prominent. AI-generated text is now prevalent across diverse fields, encompassing chatbots, content generation platforms, and automated news reporting systems, among others. This expansion has brought to the forefront the significant challenge of ensuring the credibility and trustworthiness of digital content in an era where AI-generated text is ubiquitous. The ease with which AI can produce text that closely mimics human writing has raised substantial concerns regarding the potential for forgery and manipulation, necessitating effective means to authenticate and safeguard digital textual content.
Traditional methods for detecting forged or manipulated content typically involve linguistic analysis and plagiarism detection. While these methods have been somewhat effective in identifying discrepancies and similarities in textual content, they often fall short when confronted with the sophisticated nature of forgeries created by advanced AI systems. The limitations of conventional detection techniques stem from their inability to thoroughly analyze and identify the intricate patterns and nuances that AI technologies are capable of replicating in text. As a result, there is a pressing need for a more robust and reliable approach to detecting and preventing the forgery of AI-generated text, one that can address the complex challenges posed by the advanced capabilities of AI.
In response to these challenges, the development of an "AI Text Forgery Detection Method Using Blockchain Verification" represents a significant advancement. This method integrates the precision of linguistic and structural analysis with the immutable and transparent characteristics of blockchain technology. By utilizing blockchain for timestamping, digital signature generation, and recording, this approach offers a comprehensive and secure framework for verifying the origin and authenticity of AI-generated text. The application of blockchain technology in this context provides a novel means of addressing the vulnerabilities associated with digital textual content, ensuring the credibility and integrity of AI-generated text.
Furthermore, the emergence of blockchain technology as a solution for verifying the authenticity of digital content introduces a new paradigm in the fight against text forgery and manipulation. The immutable nature of blockchain, combined with its capability for transparent and verifiable record-keeping, offers an unparalleled level of security in the authentication process. This integration of blockchain into the detection of forged AI-generated text signifies a forward-thinking approach to overcoming the limitations of traditional detection methods, ensuring a higher degree of reliability and trust in digital textual content.
In light of the above discussion, there exists an urgent need for solutions that overcome the challenges associated with conventional systems and techniques for ensuring the authenticity and integrity of AI-generated text. The "AI Text Forgery Detection Method Using Blockchain Verification" presents a pioneering solution to these challenges, offering a robust framework for the detection and prevention of text forgery and manipulation in the digital age.

Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
A system for artificial intelligence (AI) text forgery detection using blockchain verification is disclosed. Said system comprises several components designed to identify, record, and verify textual content to ensure its authenticity and to detect any signs of forgery. Central to the system's operation is a text analysis module tasked with the analysis of a text's linguistic and structural characteristics. This module delves into the intricacies of writing style, grammar, vocabulary, and the organization of the text to identify signs indicative of forgery.
In an embodiment, a timestamping and digital signature generation module is employed to further secure the authenticity of the text. By utilizing cryptographic techniques, this module assigns unique timestamps and generates digital signatures for texts. These signatures, once created, are recorded on a blockchain. The employment of a secure hash algorithm (SHA) for digital signature creation is instrumental in ensuring the high security and uniqueness of these signatures.
In an embodiment, the blockchain recording module plays a pivotal role in the preservation and verification of text authenticity. It immutably records the timestamped text along with its digital signature on a blockchain, leveraging a decentralized ledger system. This not only prevents tampering but also enhances the integrity of the data. Notably, the module records this information on a public blockchain, which affords transparency and allows for public verifiability of the text's authenticity.
In an embodiment, the forgery detection module is tasked with authenticating text. It achieves this by comparing newly computed digital signatures against those recorded on the blockchain. Authenticity is verified based on a match of the signatures, and any mismatch flags the potential for forgery. This module is further configured to automatically retrieve text and its digital signature from the blockchain at predetermined intervals, thus ensuring continuous monitoring and verification of text authenticity.
In an embodiment, an alert system is configured within the system to address potential forgery detections. Upon detecting signs of forgery, this system is capable of issuing notifications, thereby enabling prompt intervention. Such notifications can be directed towards relevant authorities or parties, facilitating immediate action. The alert system includes options for automatic escalation to law enforcement or regulatory bodies in the event of detected high-risk or high-impact forgery attempts.
In an embodiment, the system is enhanced by the inclusion of a user interface module. This module allows users to input text for analysis and displays the results of the forgery detection process. It serves to streamline the user experience by providing an accessible platform for text submission and review of analysis outcomes.
In an embodiment, the integration of machine learning algorithms into the text analysis module is a significant advancement. These algorithms enhance the accuracy of linguistic and structural analysis, enabling a more precise identification of forgery signs. This enhancement underscores the system's reliance on cutting-edge technology to improve its forgery detection capabilities.
In an embodiment, the adoption of smart contracts by the blockchain recording module introduces automation into the verification process. This reduces the need for manual intervention, thereby accelerating the detection of forgery. The automation provided by smart contracts exemplifies the system's innovative use of blockchain technology to further its objectives.
In an embodiment, the system's components, including the text analysis module, timestamping and digital signature generation module, blockchain recording module, and forgery detection module, are integrated into a single software application. This integration streamlines operations and enhances the overall user experience, demonstrating the system's commitment to operational efficiency and user satisfaction.
A method for detecting text forgery utilizing artificial intelligence (AI) and blockchain verification is disclosed, incorporating a comprehensive approach that starts with the analysis of text by a text analysis module to identify forgery signs through linguistic and structural examination. Following this, a timestamping and digital signature generation module assigns timestamps and creates digital signatures using cryptographic techniques, ensuring the signatures' uniqueness and security with a secure hash algorithm (SHA). These signatures are then immutably recorded on a blockchain by a blockchain recording module, leveraging a decentralized ledger to safeguard data integrity. Authenticity is verified by a forgery detection module that compares newly computed digital signatures against those stored on the blockchain, flagging any mismatches as potential forgeries. The process culminates with an alert system issuing notifications to relevant authorities or parties upon detection of forgery, enabling prompt intervention. This method represents a robust framework for the secure verification of text authenticity, integrating advanced AI analysis with the immutable record-keeping capabilities of blockchain technology to effectively combat text forgery.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a system for artificial intelligence (AI) text forgery detection using blockchain verification, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method for detecting text forgery using artificial intelligence (AI) and blockchain verification, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a workflow diagram for detecting text forgery using artificial intelligence (AI) and blockchain verification, in accordance with the embodiments of the present disclosure.
Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate 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 herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a system (100) for artificial intelligence (AI) text forgery detection using blockchain verification, in accordance with the embodiments of the present disclosure. The system (100) for artificial intelligence (AI) text forgery detection using blockchain verification includes a text analysis module (102) designed for the intricate task of analyzing texts to discern linguistic and structural characteristics indicative of forgery. The linguistic analysis aspect of this module delves deep into evaluating the writing style, grammar, and vocabulary used in a text. This involves a comprehensive examination of the way sentences are constructed, the complexity or simplicity of language employed, and the consistency of grammar usage, all of which are telltale signs that can either affirm the authenticity of a text or raise suspicions about its genuineness. Parallelly, the structural analysis focuses on how the text is organized, looking at paragraph structures, the coherence of ideas, and the logical flow of content. This dual-faceted approach ensures a thorough vetting process, wherein texts undergo a rigorous scrutiny process. The purpose of such detailed analysis is to identify discrepancies that might not be apparent at a cursory glance but are indicative of attempts to manipulate or forge documents. The ultimate goal of the text analysis module is to facilitate a determination based on forgery indications, effectively filtering out manipulated texts from genuine ones. By employing advanced AI techniques to analyze texts deeply, this module plays a pivotal role in the early stages of forgery detection, setting the groundwork for further verification steps within the system.
The timestamping and digital signature generation module (104) serves a critical role within the system by implementing cryptographic techniques to assign unique timestamps and create digital signatures for texts. This module encapsulates the essence of marrying traditional cryptographic practices with the innovative realm of blockchain technology. The creation of digital signatures involves generating a cryptographic hash of the text, which is then encrypted with a private key to ensure that the signature is both unique and securely tied to the text it represents. The assignment of timestamps is equally vital, providing a verifiable record of when the text was created or analyzed, thus establishing a chronological integrity for documents. Once generated, these digital signatures are recorded on a blockchain, a step that leverages the blockchain's inherent characteristics of immutability and transparency. This action not only secures the authenticity of the text but also ensures that any subsequent verification process can rely on the unaltered and time-stamped proof of the text's origination or analysis. The integration of timestamping and digital signature generation is fundamental to the system's ability to provide a robust defense against forgery, ensuring that every piece of text can be traced back to its origin with a verifiable, cryptographic proof of authenticity.
Incorporating a blockchain recording module (106) into the system introduces a layer of security and integrity that is paramount in the fight against text forgery. This module is tasked with the immutable recording of texts along with their corresponding digital signatures on a blockchain. The process of recording on a blockchain utilizes a decentralized ledger technology, which is distributed across multiple nodes, making it nearly impossible to alter recorded data without consensus from a majority of the network. This characteristic of blockchain technology ensures that once a text and its digital signature are recorded, they remain unchangeable and permanently verifiable. This immutability is crucial for maintaining the integrity of documents, providing a secure and transparent method for storing records that can stand the test of time and scrutiny. The blockchain recording module thus acts as a guardian of authenticity, leveraging the decentralized and tamper-evident nature of blockchain to protect against the unauthorized alteration of texts. By ensuring that every recorded text is permanently etched into the blockchain, the system provides a foundational trust layer, reassuring users that the texts' authenticity can always be verified against an unalterable record.
The forgery detection module (108) is a cornerstone of the system, equipped with the capability to authenticate texts through a meticulous comparison of digital signatures. Upon the creation or analysis of a text, a unique digital signature is generated and recorded on the blockchain. When a text is presented for verification, the forgery detection module computes a new digital signature for the text and compares it against the signature previously recorded on the blockchain. This comparison is critical; a match between the signatures confirms the text's authenticity, while any mismatch is indicative of potential forgery. The detection of a mismatch triggers the system's security protocols, flagging the text as potentially forged. This module embodies the system's final line of defense against text forgery, operationalizing the cryptographic and blockchain technologies to not only detect but also deter fraudulent activities. Through this sophisticated mechanism of signature comparison, the module ensures a high level of security, making it exceedingly difficult for forgers to pass off manipulated texts as authentic. The forgery detection module's effectiveness lies in its ability to utilize the immutable records on the blockchain as a benchmark for authenticity, thus playing a pivotal role in maintaining the integrity of texts within the system.
Finally, the alert system (110) is strategically integrated into the system to provide real-time notifications in the event of potential forgery detection. This module is designed to act swiftly upon the identification of a discrepancy by the forgery detection module. The issuance of alerts is a critical feature that enables prompt intervention, allowing relevant authorities or parties to take immediate action. The alert system is configured to ensure that notifications are not only prompt but also delivered to the appropriate stakeholders, thereby facilitating a rapid response to mitigate the potential impact of forged documents. This proactive approach in notifying relevant parties allows for a coordinated effort to address and resolve issues related to text forgery, ensuring that measures can be taken to protect the integrity of the documents and the interests of those affected. The inclusion of an alert system underscores the system's commitment to not just detect forgeries but also to act decisively in preventing the circulation of forged texts, thus reinforcing the overall security framework of the system.
In an embodiment, the text analysis module (102) utilizes machine learning algorithms to enhance the accuracy of linguistic and structural analysis for identifying signs of forgery. Machine learning algorithms are trained on vast datasets containing examples of both authentic and forged texts, enabling the system to learn and identify subtle patterns and anomalies that may indicate fraudulent activity. The integration of these algorithms allows for the continuous improvement of the module's detection capabilities as it processes more data, effectively adapting to new and evolving forgery techniques. This dynamic approach significantly increases the precision of linguistic and structural analyses, enabling the system to distinguish between genuine and manipulated texts with a high degree of confidence. Through the application of machine learning, the text analysis module becomes more adept at identifying complex forgery signs that may elude traditional analysis methods, thus bolstering the system's overall effectiveness in combating text-based fraud.
In an embodiment, the timestamping and digital signature generation module (104) employs a secure hash algorithm (SHA) for the creation of digital signatures, ensuring high security and uniqueness of signatures. The use of SHA, a cryptographic hash function, provides a means to generate a unique hash value from the original text, which is then used to create the digital signature. This process ensures that each digital signature is both secure and distinct, significantly reducing the risk of signature duplication or forgery. The employment of SHA in digital signature creation enhances the cryptographic security of the system, ensuring that the digital signatures are tamper-proof and verifiable. By leveraging the robust security features of SHA, the module guarantees the integrity and authenticity of digital signatures, playing a crucial role in maintaining the trustworthiness of the text authentication process.
In an embodiment, the system further comprises a user interface module (112) configured to allow users to input text for analysis and to display the results of the forgery detection process. This module serves as the primary interaction point between the system and its users, offering a user-friendly interface for the submission of texts for authenticity verification. The user interface module facilitates ease of use, enabling users to conveniently input text documents and receive immediate feedback on the analysis. The results of the forgery detection process, including any indications of forgery, are displayed in an accessible and understandable format, allowing users to comprehend the analysis outcome effectively. The incorporation of a user interface module significantly enhances the system's usability, making sophisticated forgery detection technology accessible to a wider audience without the need for specialized knowledge.
In an embodiment, the blockchain recording module (106) records the timestamped text and its digital signature on a public blockchain, providing transparency and public verifiability of the text authenticity. The use of a public blockchain for recording ensures that the authenticity of texts can be verified by any party without requiring access to proprietary or confidential systems. This openness fosters a transparent environment where the integrity of text documents can be independently confirmed, enhancing the trust in the documents' authenticity among all stakeholders. Recording on a public blockchain not only secures the data against tampering but also democratizes the verification process, making it accessible to anyone interested in validating the authenticity of a text, thereby contributing to the establishment of a more transparent and trustworthy digital environment.
In an embodiment, the forgery detection module (108) is configured to automatically retrieve text and its digital signature from the blockchain at predetermined intervals, ensuring continuous monitoring and authenticity verification. This proactive approach to forgery detection allows the system to maintain a vigilant watch over the texts recorded on the blockchain, enabling the early detection of any unauthorized changes or attempts at forgery. The automatic retrieval of texts and their digital signatures facilitates a consistent verification process, minimizing the risk of oversight and enhancing the system's ability to protect the integrity of text documents. By ensuring continuous monitoring, the forgery detection module provides an added layer of security, reinforcing the system's commitment to upholding the authenticity of digital texts in an ever-changing digital landscape.
In an embodiment, the alert system (110) includes options for automatic escalation to law enforcement or regulatory bodies in cases of detected high-risk or high-impact forgery attempts. This feature allows for the immediate notification of relevant authorities when the system identifies a potential forgery that poses a significant threat or has far-reaching implications. The capability to automatically escalate such detections streamlines the process of alerting the appropriate entities, ensuring that potential forgeries are addressed promptly and effectively. This mechanism not only aids in the swift resolution of forgery incidents but also serves as a deterrent against the commission of such fraudulent activities, knowing that high-risk attempts will be quickly escalated to authorities for investigation and action.
In an embodiment, the blockchain recording module (106) incorporates smart contracts to automate the verification process, reducing the need for manual intervention and accelerating the detection of forgery. Smart contracts are self-executing contracts with the terms of the agreement directly written into lines of code. In the context of the blockchain recording module, these contracts automatically verify the authenticity of texts and digital signatures based on pre-defined criteria, facilitating an efficient and autonomous verification process. The automation of verification through smart contracts enhances the system's efficiency, enabling faster response times in the identification and flagging of potential forgeries. This reduction in manual processes not only speeds up the verification but also minimizes the possibility of human error, ensuring a more reliable and consistent approach to forgery detection.
In an embodiment, the text analysis module (102), timestamping and digital signature generation module (104), blockchain recording module (106), and forgery detection module (108) are integrated into a single software application, streamlining operations and enhancing user experience. The integration of these modules into a cohesive software application simplifies the system's architecture, making it easier for users to navigate and utilize the system's features. This unified approach facilitates seamless interaction between the different components of the system, ensuring efficient operation and a smoother user experience. By consolidating the functionalities into a single application, the system offers a comprehensive solution for text forgery detection and verification, embodying a user-centric design that prioritizes accessibility and convenience for its users.
FIG. 2 illustrates a method (200) for detecting text forgery using artificial intelligence (AI) and blockchain verification, in accordance with the embodiments of the present disclosure. At step (202) the text analysis module (102) initiates the process by scrutinizing the text's linguistic and structural characteristics, aiming to uncover any discrepancies or anomalies that might indicate attempts at forgery. This detailed examination forms the foundational step in identifying potential text manipulations. At step (204) the timestamping and digital signature generation module (104) assigns a specific timestamp to the text and generates a unique digital signature through cryptographic techniques. This step ensures each text is uniquely identified and secured. At step (206) the blockchain recording module (106) then takes the timestamped text and its digital signature, recording them on a blockchain. This action ensures the data is stored in an immutable form, safeguarding against unauthorized alterations. At step (208) the forgery detection module (108) engages in the process of authenticating the text. It computes a new digital signature for the text and compares it with the one previously recorded on the blockchain, determining the text's authenticity based on this comparison. At step (210) upon identifying discrepancies that suggest potential forgery, the alert system (110) is triggered to issue notifications. These alerts are designed to inform relevant authorities or parties, facilitating immediate and appropriate actions to address the forgery attempt.
FIG. 3 illustrates a workflow diagram for detecting text forgery using artificial intelligence (AI) and blockchain verification, in accordance with the embodiments of the present disclosure. The process commences with an analysis of text by the text analysis module for signs of forgery, focusing on linguistic and structural attributes. If signs of forgery are detected, the process advances to the timestamping and digital signature generation phase, where the text is assigned a timestamp, and a cryptographic digital signature is created. This signature, along with the timestamped text, is then recorded on a blockchain, ensuring immutability and security of the record. Following this, the forgery detection module retrieves the timestamp and signature from the blockchain to verify the text's authenticity. If the comparison confirms that the text is authentic, the process concludes. However, if the text is determined to be inauthentic, the system triggers an alert notification to inform relevant parties or authorities about the potential forgery, enabling them to take prompt action. This completes the cycle of forgery detection and notification as outlined in the depicted workflow.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
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.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claims:

A system (100) for artificial intelligence (AI) text forgery detection using blockchain verification, comprising:
a text analysis module (102) for analyzing text's linguistic and structural characteristics to identify forgery signs, wherein linguistic analysis examines writing style, grammar, and vocabulary, and structural analysis examines text organization, leading to a determination step based on forgery indication;
a timestamping and digital signature generation module (104) for assigning timestamps and creating digital signatures via cryptographic techniques, with said signatures recorded on a blockchain to denote text creation or analysis time;
a blockchain recording module (106) for immutably recording the timestamped text and its digital signature on a blockchain, utilizing a decentralized ledger system to ensure data integrity and prevent tampering;
a forgery detection module (108) for authenticating text by comparing a newly computed digital signature against the recorded signature on the blockchain, with authenticity verification based on signature match, and potential forgery flagged upon mismatch; and
an alert system (110) configured to issue notifications upon potential forgery detection, enabling prompt intervention by notifying relevant authorities or parties.
The system of claim 1, wherein the text analysis module (102) utilizes machine learning algorithms to enhance the accuracy of linguistic and structural analysis for identifying forgery signs.
The system of claim 1, wherein the timestamping and digital signature generation module (104) employs a secure hash algorithm (SHA) for digital signature creation, ensuring high security and uniqueness of signatures.
The system of claim 1, further comprising a user interface module (112) configured to allow users to input text for analysis and to display the results of the forgery detection process.
The system of claim 1, wherein the blockchain recording module (106) records the timestamped text and its digital signature on a public blockchain, providing transparency and public verifiability of the text authenticity.
The system of claim 1, wherein the forgery detection module (108) is configured to automatically retrieve text and its digital signature from the blockchain at predetermined intervals, ensuring continuous monitoring and authenticity verification.
The system of claim 1, wherein the alert system (110) includes options for automatic escalation to law enforcement or regulatory bodies in cases of detected high-risk or high-impact forgery attempts.
The system of claim 1, wherein the blockchain recording module (106) incorporates smart contracts to automate the verification process, reducing the need for manual intervention and accelerating the detection of forgery.
The system of claim 1, wherein the text analysis module (102), timestamping and digital signature generation module (104), blockchain recording module (106), and forgery detection module (108) are integrated into a single software application, streamlining operations and enhancing user experience.
A method for detecting text forgery using artificial intelligence (AI) and blockchain verification, the method comprising:
analyzing, by a text analysis module (102), the linguistic and structural characteristics of text to identify signs of forgery;
assigning, by a timestamping and digital signature generation module (104), a timestamp and generating a digital signature for the text using cryptographic techniques, and recording said timestamp and digital signature on a blockchain;
recording, by a blockchain recording module (106), the timestamped text and its digital signature on a blockchain in an immutable manner;
authenticating, by a forgery detection module (108), the text by comparing a newly computed digital signature against the recorded signature on the blockchain and determining text authenticity based on the match or mismatch of signatures; and
issuing, by an alert system (110), notifications upon detection of potential forgery to enable prompt intervention by relevant authorities or parties.

TEXT FORGERY DETECTION SYSTEM

The present disclosure provides a system for artificial intelligence (AI) text forgery detection using blockchain verification, comprising: a text analysis module for analyzing text's linguistic and structural characteristics to identify forgery signs, wherein linguistic analysis examines writing style, grammar, and vocabulary, and structural analysis examines text organization, leading to a determination step based on forgery indication; a timestamping and digital signature generation module for assigning timestamps and creating digital signatures via cryptographic techniques, with said signatures recorded on a blockchain to denote text creation or analysis time; a blockchain recording module for immutably recording the timestamped text and its digital signature on a blockchain, utilizing a decentralized ledger system to ensure data integrity and prevent tampering; a forgery detection module for authenticating text by comparing a newly computed digital signature against the recorded signature on the blockchain, with authenticity verification based on signature match, and potential forgery flagged upon mismatch; and an alert system configured to issue notifications upon potential forgery detection, enabling prompt intervention by notifying relevant authorities or parties.
Fig. 1

Drawings
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FIG. 1
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FIG. 2
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FIG. 3

, Claims:I/We claims:

A system (100) for artificial intelligence (AI) text forgery detection using blockchain verification, comprising:
a text analysis module (102) for analyzing text's linguistic and structural characteristics to identify forgery signs, wherein linguistic analysis examines writing style, grammar, and vocabulary, and structural analysis examines text organization, leading to a determination step based on forgery indication;
a timestamping and digital signature generation module (104) for assigning timestamps and creating digital signatures via cryptographic techniques, with said signatures recorded on a blockchain to denote text creation or analysis time;
a blockchain recording module (106) for immutably recording the timestamped text and its digital signature on a blockchain, utilizing a decentralized ledger system to ensure data integrity and prevent tampering;
a forgery detection module (108) for authenticating text by comparing a newly computed digital signature against the recorded signature on the blockchain, with authenticity verification based on signature match, and potential forgery flagged upon mismatch; and
an alert system (110) configured to issue notifications upon potential forgery detection, enabling prompt intervention by notifying relevant authorities or parties.
The system of claim 1, wherein the text analysis module (102) utilizes machine learning algorithms to enhance the accuracy of linguistic and structural analysis for identifying forgery signs.
The system of claim 1, wherein the timestamping and digital signature generation module (104) employs a secure hash algorithm (SHA) for digital signature creation, ensuring high security and uniqueness of signatures.
The system of claim 1, further comprising a user interface module (112) configured to allow users to input text for analysis and to display the results of the forgery detection process.
The system of claim 1, wherein the blockchain recording module (106) records the timestamped text and its digital signature on a public blockchain, providing transparency and public verifiability of the text authenticity.
The system of claim 1, wherein the forgery detection module (108) is configured to automatically retrieve text and its digital signature from the blockchain at predetermined intervals, ensuring continuous monitoring and authenticity verification.
The system of claim 1, wherein the alert system (110) includes options for automatic escalation to law enforcement or regulatory bodies in cases of detected high-risk or high-impact forgery attempts.
The system of claim 1, wherein the blockchain recording module (106) incorporates smart contracts to automate the verification process, reducing the need for manual intervention and accelerating the detection of forgery.
The system of claim 1, wherein the text analysis module (102), timestamping and digital signature generation module (104), blockchain recording module (106), and forgery detection module (108) are integrated into a single software application, streamlining operations and enhancing user experience.
A method for detecting text forgery using artificial intelligence (AI) and blockchain verification, the method comprising:
analyzing, by a text analysis module (102), the linguistic and structural characteristics of text to identify signs of forgery;
assigning, by a timestamping and digital signature generation module (104), a timestamp and generating a digital signature for the text using cryptographic techniques, and recording said timestamp and digital signature on a blockchain;
recording, by a blockchain recording module (106), the timestamped text and its digital signature on a blockchain in an immutable manner;
authenticating, by a forgery detection module (108), the text by comparing a newly computed digital signature against the recorded signature on the blockchain and determining text authenticity based on the match or mismatch of signatures; and
issuing, by an alert system (110), notifications upon detection of potential forgery to enable prompt intervention by relevant authorities or parties.

TEXT FORGERY DETECTION SYSTEM