Knowledge-enhanced Black-box Attacks for Recommendations

• URL: http://arxiv.org/abs/2207.10307v1
• Date: Thu, 21 Jul 2022 04:59:31 GMT
• Title: Knowledge-enhanced Black-box Attacks for Recommendations
• Authors: Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang
• Abstract summary: Deep neural networks-based recommender systems are vulnerable to adversarial attacks. We propose a knowledge graph-enhanced black-box attacking framework (KGAttack) to effectively learn attacking policies. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed attacking framework.
• Score: 21.914252071143945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items. Due to the security and privacy concerns, it is more practical to perform adversarial attacks under the black-box setting, where the architecture/parameters and training data of target systems cannot be easily accessed by attackers. However, generating high-quality fake user profiles under black-box setting is rather challenging with limited resources to target systems. To address this challenge, in this work, we introduce a novel strategy by leveraging items' attribute information (i.e., items' knowledge graph), which can be publicly accessible and provide rich auxiliary knowledge to enhance the generation of fake user profiles. More specifically, we propose a knowledge graph-enhanced black-box attacking framework (KGAttack) to effectively learn attacking policies through deep reinforcement learning techniques, in which knowledge graph is seamlessly integrated into hierarchical policy networks to generate fake user profiles for performing adversarial black-box attacks. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed attacking framework under the black-box setting.

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