Ownership Verification of Your NLG Models With Semantic Combination Watermarks

Résumé fourni par la source

Natural Language Generation (NLG) applications have gained immense popularity due to the utilization of powerful deep learning techniques and large training corpora. However, the increasing prevalence of NLG models also poses a significant risk of unauthorized access or theft of intellectual property (IP). To safeguard NLG models, watermarking has emerged as a promising tool, but existing watermarking techniques based on pre-processing are prone to attacker detection and can potentially harm NLG applications. This paper proposes a novel, semantic, and stealthy watermarking scheme for IP protection of NLG models. Our approach embeds a semantic combination water mark, which is generated through a multi-stage process designed to be semantic and stealthy. This scheme endows an NLG model with a verifiable preference for specific semantic combinations, which are initiated by a foundational pattern but holistically constructed to preserve model functionality. To enhance the robustness, data embedding is systematically performed through a masked location injection. Consequently, the watermark is seamlessly integrated into NLG models without misleading their original attention mechanism. Comprehensive experiments are conducted to demonstrate that the proposed scheme is highly effective and robust in protecting the IP of NLG models while remaining stealthy to potential attackers.

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