Preserving Structure, Strengthening Signals: A Grammar-Driven Approach to Optimize the Trade-off Between Quality and Detectability in Code Watermarking

Abstract

Text watermarking has emerged as a crucial technique for identifying AI-generated content. However, current watermarking schemes entail a trade-off between text quality and watermark detectability, which is further intensified by the inherent low-entropy nature of code. To mitigate this dilemma, we propose Grammar-Driven Watermark (GDW), a training-free watermarking approach tailored to LLM-based code generation. GDW employs a three-level masking mechanism consisting of a green-list mask, a syntactic validity mask, and a structural role mask to regulate watermark injection. These masks are integrated through hierarchical modulation, which limits the scope of watermark injection to syntactically admissible green-list tokens and adjusts watermark strength based on structural roles through a role function. Accordingly, we design a role-aware weighted detection statistic. For comprehensive evaluation, we introduce the Area Under the Trade-off Curve (AUTC), a metric that quantifies the quality-detectability trade-off. Extensive experiments demonstrate that GDW outperforms existing baselines and achieves state-of-the-art (SOTA) trade-off performance, while maintaining superior performance under seven semantics-preserving attacks.