TL;DR
GREW introduces a stealthy watermarking framework for recommender systems that embeds ownership signals into the ranking process, enhancing robustness against attacks without synthetic data injection.
Contribution
The paper proposes GREW, a novel watermarking method that uses secret key-controlled item partitioning and ranking integration to improve security and stealthiness in recommender systems.
Findings
GREW achieves high ownership verification accuracy.
GREW demonstrates robustness against model extraction attacks.
GREW requires no synthetic data injection.
Abstract
The widespread open-sourcing of advanced recommendation algorithms and the rising threat of model extraction attacks have made safeguarding the intellectual property of recommender systems an imperative task. While watermarking serves as a potent defense, existing methods primarily rely on forcing models to memorize pre-defined interaction patterns. Such memorization-based approaches often require excessive synthetic data injection and are vulnerable to removal attacks due to their detectable statistical deviations from natural user behavior. To address these limitations, we propose GREW, a novel Green-REd Watermarking framework for recommender systems. GREW leverages a secret key to partition the item space into "green" items for soft promotion and "red" items as anchors, thereby shifting the paradigm from fragile memorization to a stealthy, key-controlled output bias. By integrating…
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