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PRADA: Practical Black-box Adversarial Attacks against Neural Ranking Models

文献类型：期刊论文

作者Wu, Chen2,3; Zhang, Ruqing2,3; Guo, Jiafeng2,3; De Rijke, Maarten1; Fan, Yixing3,4; Cheng, Xueqi3,4
刊名ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版日期2023-10-01
卷号41期号:4页码:27
ISSN号1046-8188
关键词Adversarial attack decision-based black-box attack setting neural ranking models
DOI10.1145/3576923
英文摘要Neural ranking models (NRMs) have shown remarkable success in recent years, especially with pre-trained language models. However, deep neural models are notorious for their vulnerability to adversarial examples. Adversarial attacks may become a new type of web spamming technique given our increased reliance on neural information retrieval models. Therefore, it is important to study potential adversarial attacks to identify vulnerabilities of NRMs before they are deployed. In this article, we introduce the Word Substitution Ranking Attack (WSRA) task against NRMs, which aims at promoting a target document in rankings by adding adversarial perturbations to its text. We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list. This attack setting is realistic in real-world search engines. We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients for finding the adversarial perturbations. Experiments on two web search benchmark datasets show that PRADA can outperform existing attack strategies and successfully fool the NRM with small indiscernible perturbations of text.
资助项目National Natural Science Foundation of China (NSFC)[62006218] ; National Natural Science Foundation of China (NSFC)[61902381] ; Youth Innovation Promotion Association CAS[20144310] ; Youth Innovation Promotion Association CAS[2021100] ; Young Elite Scientist Sponsorship Program by CAST[YESS20200121] ; Lenovo-CAS Joint Lab Youth Scientist Project ; Hybrid Intelligence Center, a 10-year program - Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:001068685300008
源URL[http://119.78.100.204/handle/2XEOYT63/21123]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Guo, Jiafeng
作者单位1.Univ Amsterdam, NL-1012WX Amsterdam, Netherlands
2.Inst Comp Technol Acad Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Chen,Zhang, Ruqing,Guo, Jiafeng,et al. PRADA: Practical Black-box Adversarial Attacks against Neural Ranking Models[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2023,41(4):27.
APA Wu, Chen,Zhang, Ruqing,Guo, Jiafeng,De Rijke, Maarten,Fan, Yixing,&Cheng, Xueqi.(2023).PRADA: Practical Black-box Adversarial Attacks against Neural Ranking Models.ACM TRANSACTIONS ON INFORMATION SYSTEMS,41(4),27.
MLA Wu, Chen,et al."PRADA: Practical Black-box Adversarial Attacks against Neural Ranking Models".ACM TRANSACTIONS ON INFORMATION SYSTEMS 41.4(2023):27.

入库方式： OAI收割

来源：计算技术研究所

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