arXiv:2606.00392v1 Announce Type: new Abstract: AI-text detectors are vulnerable to paraphrasing and detector-guided paraphrasing attacks, but existing detector-evasion methods often lack precise control over semantic preservation. In particular, optimizing directly for detector evasion can degrade fine-grained semantics, whereas scalarized reward designs provide only indirect, weight-sensitive control over the evasion-semantics trade-off. We address this limitation by formulating detector-evasive LLM paraphrasing as a Constrained Markov Decision Process, where detector evasion is the primary
The proliferation of AI-generated text makes the development of robust detection mechanisms and counter-evasion strategies a critical and immediate concern.
This research highlights the escalating arms race between AI text detection and evasion, which has significant implications for information integrity, content moderation, and the trustworthiness of digital communication.
A new method for LLM paraphrasing aims to evade detectors while preserving semantic integrity, moving beyond indirect control to a more precise, constrained optimization approach.
- · AI content creators
- · Adversarial AI researchers
- · AI text detector developers
- · Content moderation platforms
AI-generated text becomes harder to consistently identify, complicating content provenance.
The cost and complexity of effective AI content moderation increase significantly, requiring more advanced counter-evasion techniques.
Public trust in digital information erodes further as the distinction between human and AI-generated content blurs due to sophisticated evasion capabilities.
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Read at arXiv cs.LG