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

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 context-aware robust watermarking framework that dynamically adjusts watermark embedding strength according to contextual semantic…