Watermarking Recommender Systems
arxiv(2024)
摘要
Recommender systems embody significant commercial value and represent crucial
intellectual property. However, the integrity of these systems is constantly
challenged by malicious actors seeking to steal their underlying models.
Safeguarding against such threats is paramount to upholding the rights and
interests of the model owner. While model watermarking has emerged as a potent
defense mechanism in various domains, its direct application to recommender
systems remains unexplored and non-trivial. In this paper, we address this gap
by introducing Autoregressive Out-of-distribution Watermarking (AOW), a novel
technique tailored specifically for recommender systems. Our approach entails
selecting an initial item and querying it through the oracle model, followed by
the selection of subsequent items with small prediction scores. This iterative
process generates a watermark sequence autoregressively, which is then
ingrained into the model's memory through training. To assess the efficacy of
the watermark, the model is tasked with predicting the subsequent item given a
truncated watermark sequence. Through extensive experimentation and analysis,
we demonstrate the superior performance and robust properties of AOW. Notably,
our watermarking technique exhibits high-confidence extraction capabilities and
maintains effectiveness even in the face of distillation and fine-tuning
processes.
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