ML Security PapersAI Safeguards, Generative AI and the Pandora Box: AI Safety Measures to Protect Businesses and Personal Reputation
Published on arXiv
2601.06197
Output Integrity Attack
OWASP ML Top 10 — ML09
Key Finding
TCN models trained with Temporal Consistency Learning outperform baseline approaches in detecting five categories of generative AI dark-side problems
TCL (Temporal Consistency Learning)
Novel technique introduced
Generative AI has unleashed the power of content generation and it has also unwittingly opened the pandora box of realistic deepfake causing a number of social hazards and harm to businesses and personal reputation. The investigation & ramification of Generative AI technology across industries, the resolution & hybridization detection techniques using neural networks allows flagging of the content. Good detection techniques & flagging allow AI safety - this is the main focus of this paper. The research provides a significant method for efficiently detecting dark side problems by imposing a Temporal Consistency Learning (TCL) technique. Through pretrained Temporal Convolutional Networks (TCNs) model training and performance comparison, this paper showcases that TCN models outperforms the other approaches and achieves significant accuracy for five dark side problems. Findings highlight how important it is to take proactive measures in identification to reduce any potential risks associated with generative artificial intelligence.
Key Contributions
- Introduces Temporal Consistency Learning (TCL) as a detection framework for generative AI harms including deepfakes
- Evaluates pretrained Temporal Convolutional Networks (TCNs) against alternative approaches across five categories of generative AI misuse
- Demonstrates TCN superiority in detection accuracy for AI safety applications protecting business and personal reputation
🛡️ Threat Analysis
The paper's core contribution is a detection methodology (TCL with pretrained TCNs) for identifying AI-generated harmful content, specifically deepfakes — a canonical ML09 output integrity / AI-generated content detection task. The paper benchmarks this against other approaches across five 'dark side' generative AI problem categories.