Adaptive Robust Watermarking for Large Language Models via Dynamic Token Embedding Perturbation

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in generating high-quality text, raising significant concerns regarding copyright protection and content provenance verification. However, most existing watermarking techniques rely on uniform perturbation or rule-based token biasing schemes, which exhibit critical vulnerabilities under adversarial attacks such as paraphrasing, translation, and content truncation, often failing to maintain detection reliability in real-world deployment scenarios. To address these challenges, this paper introduces a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">context-aware robust watermarking framework</i> that dynamically adjusts watermark embedding strength according to contextual semantic characteristics during text generation. The proposed approach incorporates a token-level semantic modulation mechanism that strategically intensifies watermark signals in copyright-sensitive segments while minimizing perturbations in semantically neutral regions, achieving an improved balance between imperceptibility and robustness. Furthermore, an adaptive threshold estimation algorithm is developed for watermark detection, which automatically calibrates detection boundaries based on noise statistics, significantly enhancing resilience against diverse attack vectors. Extensive experiments on the WaterBench benchmark demonstrate superior performance over state-of-the-art baselines, maintaining high detection accuracy with a 95.3% true positive rate (TPR) under clean conditions and strong robustness under severe perturbations, including paraphrasing attacks (82.7% TPR), translation attacks (78.4% TPR), and content truncation (88.9% TPR at 50% retention). Meanwhile, the proposed method reduces false positive rates by 43.2% compared with existing approaches while preserving text quality with negligible perplexity increase (1.8%). These results establish a new paradigm for practical and scalable LLM watermarking in real-world copyright-sensitive deployment scenarios.

Topics & Concepts