Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models

doi:10.1109/TCSS.2022.3204120

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models
	Zhang, Xingwei 1,2; Zheng, Xiaolong 1,2; Mao, Wenji 1,2; Zeng, Daniel Dajun 1,2; Wang, Fei-Yue 1,2
刊名	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
	2022-09-30
页码	11
关键词	Perturbation methods Semantics Computational modeling Codes Task analysis Privacy Software Adversarial perturbation deep neural network (DNN) privacy protection semantic retrieval
ISSN号	2329-924X
DOI	10.1109/TCSS.2022.3204120
通讯作者	Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
英文摘要	The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semantic similarity matching. Hence, executing effective privacy protection mechanisms against those retrieval software is essential for reliable social website construction. In this article, we propose a retrieval task-based adversarial perturbation generation method called Hashing Fake to meet this request. Specifically, DNNs are recently found to be vulnerable to a specific set of attacks called adversarial perturbations, which denote some magnitude-restricted signals added on objective samples to misguide well-crafted DNN models, and perturbations' magnitudes are small enough that will not induce humans' perception. Moreover, since existing adversarial perturbation generation methods are designed for supervised tasks, Hashing Fake constructs a differential approximation substitution for perturbation production on unsupervised retrieval tasks. Through extensive experiments on several deep retrieval benchmarks, we demonstrate that well-crafted perturbations using Hashing Fake can effectively misguide objective models' recognitions to make false predictions. The added norm-restricted perturbations on objective samples will not alter humans' perception; hence, Hashing Fake can be applied on real-world social websites to protect subscribers' privacy against malicious retrieval software.
资助项目	Ministry of Science and Technology of China[2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[71621002]
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000865074900001
资助机构	Ministry of Science and Technology of China ; Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50352]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Zheng, Xiaolong
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xingwei,Zheng, Xiaolong,Mao, Wenji,et al. Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2022:11.
APA	Zhang, Xingwei,Zheng, Xiaolong,Mao, Wenji,Zeng, Daniel Dajun,&Wang, Fei-Yue.(2022).Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,11.
MLA	Zhang, Xingwei,et al."Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022):11.