Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models
Zhang, Xingwei1,2; Zheng, Xiaolong1,2; Mao, Wenji1,2; Zeng, Daniel Dajun1,2; Wang, Fei-Yue1,2
Source PublicationIEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN2329-924X
2022-09-30
Pages11
Corresponding AuthorZheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
AbstractThe 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.
KeywordPerturbation methods Semantics Computational modeling Codes Task analysis Privacy Software Adversarial perturbation deep neural network (DNN) privacy protection semantic retrieval
DOI10.1109/TCSS.2022.3204120
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[71621002]
Funding OrganizationMinistry of Science and Technology of China ; Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000865074900001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50353
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorZheng, Xiaolong
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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