Robust LLM Watermarking with Minimal Semantic Distortion for IP Protection
Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collecting input-output pairs to train a surrogate model, causing financial setbacks. Watermarks offer a promising defense to verify ownership, but existing methods often struggle with semantic distortion, factual inconsistency, and adversarial attacks. In addition, key-conditioned watermarks for provider-specific detection, especially in cross-provider and multi-user scenarios, remain largely underexplored. To address these challenges, we propose SAFESEAL, a novel key-conditioned watermarking framework that achieves strong detectability with minimal impact o...
| Authors | Kieu Dang, Phung Lai, NhatHai Phan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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