Adversarial Text Program for AI Analysis Resistance
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Solution Overview
Problem
Digital text documents transmitted through non-secure channels are vulnerable to automated analysis by unauthorized AI systems, lacking effective mechanisms to preserve semantic meaning while thwarting AI-based text analytics.
Innovation Solution
An adversarial text program identifies critical text portions impacting AI model predictions, generates semantically equivalent text to reduce confidence scores, and suggests modifications to enhance text robustness against AI analysis while maintaining original meaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital text documents are transmitted through non-secure channels, then communication efficiency and accessibility are improved, but the text contents become vulnerable to automated analysis by unauthorized third-party AI systems
Solution Approach 1:
The system applies preliminary anti-action by proactively modifying the text document before transmission to include adversarial perturbations. These perturbations are designed to preemptively counteract AI-based analysis by reducing the confidence scores of unauthorized AI systems, while maintaining the original semantic meaning and human readability of the document.
Solution Approach 2:
The system employs parameter changes by subtly altering text parameters such as word choices, sentence structures, or character-level features in ways that are imperceptible to human readers but significantly impact AI model predictions. These parameter modifications reduce the effectiveness of automated text analysis while preserving the document's intended meaning.
2Reliability
If text modifications are made to obstruct AI analysis, then protection against automated analysis is improved, but the semantic meaning of the original text may be altered
Solution Approach 1:
The system applies local quality by making targeted, localized modifications to specific portions of the text that are most susceptible to AI analysis. These modifications are carefully selected to maintain the overall semantic integrity of the document while providing effective protection against automated analysis in critical areas.
Solution Approach 2:
The system uses copying by generating semantically equivalent alternative text that preserves the original meaning while having different linguistic characteristics that reduce AI analysis effectiveness. Multiple semantically equivalent versions can be created to maintain meaning while obstructing automated analysis.
3Reliability
If adversarial text modifications are applied, then confidence scores of AI predictions are reduced, but the complexity of the text processing system increases
Solution Approach 1:
The system applies segmentation by dividing the text processing into distinct modular components: analysis component, modification component, and verification component. This segmentation allows each module to perform its specific function independently, managing complexity while achieving effective adversarial text protection.
Solution Approach 2:
The system employs feedback mechanisms to iteratively evaluate the effectiveness of text modifications and adjust them accordingly. The feedback loop monitors AI confidence scores and refines the adversarial modifications to maximize protection while minimizing unnecessary complexity in the processing system.
Data Source
AI summary
A method, computer system, and a computer program product for text data protection is provided. The present invention may include receiving a text data. The present invention may also include identifying a portion of the received text data having a highest impact on a first confidence score associated with a target model prediction. The present invention may further include generating at least one semantically equivalent text relative to the identified portion of the received text data. The present invention may also include determining that the generated at least one semantically equivalent text produces a second confidence score associated with the target model prediction that is less than the first confidence score associated with the target model prediction. The present invention may further include generating a prompt to suggest modifying the identified portion of the received text data using the generated at least one semantically equivalent text.