When Words Change but Meaning Remains: Robust Semantic-Stylistic Watermarking for LLMs

Watermarking can support provenance verification for text generated by large language models (LLMs), but many existing methods encode evidence in token-level statistics that degrade under meaning-preserving rewriting. We introduce SS-WM, a base-model-training-free watermarking framework that distributes key-conditioned evidence across a joint semantic–stylistic carrier. The semantic channel uses a projected representation derived from a frozen sentence encoder, while the stylistic channel uses distributional statistics of textual realization. A secret key selects carrier constraints, and inference-time reranking softly steers generation toward the resulting key-specific region while preserving prompt fidelity. Detection extracts the same carrier feature families from completed text and performs a keyed statistical hypothesis test, supporting candidate-key verification after paraphrasing, translation, summarization, stylistic rewriting, truncation, and partial reuse. SS-WM does not assume that individual semantic coordinates correspond to interpretable discourse structures; instead, robustness relies on empirical transformation stability and complementarity with stylistic evidence. On LLaMA-2-7B across fixed, composed, and adaptive transformations, with additional cross-model evaluation on LLaMA-2-13B, Mistral-7B, and OPT-2.7B, SS-WM improves watermark retention over the evaluated token-level and semantics-aware baselines while maintaining strong clean-text detection and limited quality degradation. Additional analyses characterize prefix-to-sequence surrogate alignment, stylistic feature contributions, wrong-key behavior, prompt compatibility, and the trade-off between logit-access and black-box deployment.