RLSpoofer trains a 4B model on 100 watermarked paraphrase pairs to spoof PF watermarks at 62% success rate, far exceeding baselines trained on up to 10,000 samples.
Optimizing adaptive attacks against watermarks for language models
4 Pith papers cite this work. Polarity classification is still indexing.
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TimeMark is a trustworthy time watermarking framework that achieves exact generation-time recovery from AI-generated content with theoretically perfect accuracy by using time-dependent cryptographic keys, random non-stored bit sequences, and two-stage encoding with error-correcting codes.
Randomized per-query key selection with single-key detection acceptance bounds forgery success rate independently of collected samples while preserving model utility.
LLM watermarking adoption is limited by misaligned stakeholder incentives; incentive-aligned approaches such as in-context watermarking can enable practical use in targeted domains like education and peer review.
citing papers explorer
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RLSpoofer: A Lightweight Evaluator for LLM Watermark Spoofing Resilience
RLSpoofer trains a 4B model on 100 watermarked paraphrase pairs to spoof PF watermarks at 62% success rate, far exceeding baselines trained on up to 10,000 samples.
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TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC
TimeMark is a trustworthy time watermarking framework that achieves exact generation-time recovery from AI-generated content with theoretically perfect accuracy by using time-dependent cryptographic keys, random non-stored bit sequences, and two-stage encoding with error-correcting codes.
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Mitigating Watermark Forgery in Generative Models via Randomized Key Selection
Randomized per-query key selection with single-key detection acceptance bounds forgery success rate independently of collected samples while preserving model utility.
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Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption
LLM watermarking adoption is limited by misaligned stakeholder incentives; incentive-aligned approaches such as in-context watermarking can enable practical use in targeted domains like education and peer review.