RLCracker is a reinforcement learning attack that erases LLM watermarks at 98.5% success rate with minimal data and generalizes across ten schemes and multiple model sizes.
Necessary and sufficient watermark for large language models
6 Pith papers cite this work. Polarity classification is still indexing.
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PASA is an embedding-space watermarking method for LLM text that uses semantic clusters and synchronized randomness to achieve robustness against paraphrasing while remaining distortion-free.
Two new constructions for multi-bit generative watermarking attain the established lower bound on miss-detection probability under worst-case false-alarm constraints, fully characterizing optimal performance via linear programming.
A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
CORE-BREW introduces constant-hit-rate embedding to produce LLRs enabling soft-decision decoding for more robust multi-bit LLM watermarking with two FPR-aware detection modes.
BREW uses block voting and window-shifting verification to reach TPR 0.965 and FPR 0.02 under 10% synonym substitution, addressing high false-positive issues in prior multi-bit LLM watermarking.
citing papers explorer
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RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks
RLCracker is a reinforcement learning attack that erases LLM watermarks at 98.5% success rate with minimal data and generalizes across ten schemes and multiple model sizes.
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PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks
PASA is an embedding-space watermarking method for LLM text that uses semantic clusters and synchronized randomness to achieve robustness against paraphrasing while remaining distortion-free.
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Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints
Two new constructions for multi-bit generative watermarking attain the established lower bound on miss-detection probability under worst-case false-alarm constraints, fully characterizing optimal performance via linear programming.
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Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
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CORE-BREW: LLR-Based Soft Decoding for Robust Multi-Bit LLM Watermarking
CORE-BREW introduces constant-hit-rate embedding to produce LLRs enabling soft-decision decoding for more robust multi-bit LLM watermarking with two FPR-aware detection modes.
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Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking
BREW uses block voting and window-shifting verification to reach TPR 0.965 and FPR 0.02 under 10% synonym substitution, addressing high false-positive issues in prior multi-bit LLM watermarking.