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Knowledge-enhanced Black-box Attacks for Recommendations

计算机科学 黑匣子 对抗制 推荐系统 集合(抽象数据类型) 深层神经网络 深度学习 图形 知识图 人工智能 计算机安全 情报检索 理论计算机科学 程序设计语言
作者
Jingfan Chen,Wenqi Fan,Guanghui Zhu,Xiangyu Zhao,Chunfeng Yuan,Qing Li,Yihua Huang
标识
DOI:10.1145/3534678.3539359
摘要

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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