The proliferation of large language models has intensified demands for reliable content attribution, yet existing watermarking techniques face a fundamental trilemma: they cannot simultaneously optimize for robustness against attacks, minimal text quality degradation, and detection efficiency. To resolve this challenge, we propose ARGH-Mark, a novel watermarking framework that integrates three synergistic innovations: (1) Anchor-synchronized phase recovery for maintaining detection integrity under insertion/deletion attacks, (2) RG-balanced vocabulary modulation that dynamically partitions lexicons via contextual hashing to preserve generation quality, and (3) Hamming-based error correction enabling single-bit error rectification through algebraic coding. Comprehensive evaluations across question answering (ELI5), summarization (CNN/DailyMail), and text generation (C4) demonstrate state-of-the-art performance: the proposed ARGH-Mark framework achieves near-perfect match rate and bit accuracy across diverse configurations, while preserving the quality of the generated text. It significantly reduces detection latency, enabling real-time extraction, and maintains high robustness against token tampering attacks through integrated Hamming error correction, ensuring reliable attribution in adversarial settings. ARGH-Mark achieves a new Pareto frontier in the watermarking design space and advances trustworthy deployment of generative AI in alignment-critical applications.

Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific input-output pairs. However, existing watermarks are easily removed when facing model stealing attacks, and make it difficult for model owners to effectively verify the copyright of stolen models. In this paper, we analyze the root cause of the failure of current watermarking methods under model stealing scenarios and then explore potential solutions. Specifically, we introduce a robust watermarking framework, DeepTracer, which leverages a novel watermark samples construction method and a same-class coupling loss constraint. DeepTracer can incur a high-coupling model between watermark task and primary task that makes adversaries inevitably learn the hidden watermark task when stealing the primary task functionality. Furthermore, we propose an effective watermark samples filtering mechanism that elaborately select watermark key samples used in model ownership verification to enhance the reliability of watermarks. Extensive experiments across multiple datasets and models demonstrate that our method surpasses existing approaches in defending against various model stealing attacks, as well as watermark attacks, and achieves new state-of-the-art effectiveness and robustness.