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Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign
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This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to reveal the signature and requires re-encoding new ones for code reuse, which potentially compromising the system's usability. To overcome these challenges, RoSeMary obtains high-quality watermarks by training the watermark insertion and extraction modules end-to-end to ensure (i) unaltered watermarked code functionality and (ii) enhanced detectability and robustness leveraging pre-trained CodeT5 as the insertion backbone to enlarge the code syntactic and variable rename transformation search space. In the deployment, RoSeMary uses zero-knowledge proofs for secure verification without revealing the underlying signatures. Extensive evaluations demonstrated RoSeMary achieves high detection accuracy while preserving the code functionality. RoSeMary is also robust against attacks and provides efficient secure watermark verification.
Forward citations
Cited by 4 Pith papers
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Multi-Channel Spread-Spectrum Code Watermarking
A training-free post-hoc code watermark embeds 24-bit identifiers via multi-channel spread-spectrum encoding over naming conventions and semantic pattern pairs, with majority voting and Reed-Solomon recovery.
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MATRIX: Multi-Layer Code Watermarking via Dual-Channel Constrained Parity-Check Encoding
MATRIX embeds multi-layer watermarks in LLM-generated code via dual-channel constrained parity-check encoding, achieving 99.2% detection accuracy with 0-0.14% functionality loss and 7.7-26.67% better attack robustness...
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SWaRL: Safeguard Code Watermarking via Reinforcement Learning
SWaRL trains code LLMs with RL using compiler correctness signals and a confidential verifier reward to embed robust, functionality-preserving watermarks that resist refactoring attacks.
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PromptMark: A Prompt-Guided Iterative-Feedback Framework for Source Code Watermarking
PromptMark is a black-box prompt-guided iterative-feedback framework that embeds statistically detectable watermarks in LLM-generated source code via naming patterns while preserving functional correctness.
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