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Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Xu Yuan Orcid Logo, Zheng Zhang Orcid Logo, Xunguang Wang Orcid Logo, Yuanbo Wu Orcid Logo

IEEE Transactions on Information Forensics and Security, Volume: 18, Pages: 4681 - 4694

Swansea University Author: Yuanbo Wu Orcid Logo

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DOI (Published version): 10.1109/tifs.2023.3297791

Abstract

Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerabil...

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Published in: IEEE Transactions on Information Forensics and Security
ISSN: 1556-6013 1556-6021
Published: Institute of Electrical and Electronics Engineers (IEEE) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64127
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spelling 2025-06-12T16:13:55.0432453 v2 64127 2023-08-24 Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval 205b1ac5a767e977bebb5d6afd770784 0000-0001-6119-058X Yuanbo Wu Yuanbo Wu true false 2023-08-24 MACS Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerability. Notably, it is challenging to efficiently distill reliable semantic representatives for deep hashing to guide adversarial learning, and thereby it hinders the enhancement of adversarial robustness of deep hashing-based retrieval models. Moreover, current researches on adversarial training for deep hashing are hard to be formalized into a unified minimax structure. In this paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the adversarial robustness of deep hashing models. Specifically, we conceive a discriminative mainstay features learning (DMFL) scheme to construct semantic representatives for guiding adversarial learning in deep hashing. Particularly, our DMFL with the strict theoretical guarantee is adaptively optimized in a discriminative learning manner, where both discriminative and semantic properties are jointly considered. Moreover, adversarial examples are fabricated by maximizing the Hamming distance between the hash codes of adversarial samples and mainstay features, the efficacy of which is validated in the adversarial attack trials. Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes. Extensive experiments on benchmark datasets show superb attack performance against the state-of-the-art algorithms, meanwhile, the proposed adversarial training can effectively eliminate adversarial perturbations for trustworthy deep hashing-based retrieval. Our code is available at https://github.com/xandery-geek/SAAT. Journal Article IEEE Transactions on Information Forensics and Security 18 4681 4694 Institute of Electrical and Electronics Engineers (IEEE) 1556-6013 1556-6021 Adversarial attack, adversarial training, trustworthy deep hashing, similarity retrieval 21 7 2023 2023-07-21 10.1109/tifs.2023.3297791 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required 2025-06-12T16:13:55.0432453 2023-08-24T10:27:43.1979525 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Xu Yuan 0000-0002-2822-9443 1 Zheng Zhang 0000-0003-1470-6998 2 Xunguang Wang 0000-0002-5330-2286 3 Yuanbo Wu 0000-0001-6119-058X 4 64127__28683__2efc994cbc0248839c0e762d3fbb4c9a.pdf 64127.ACCEPTED_VERSION.pdf 2023-10-03T10:55:03.3462243 Output 17820423 application/pdf Accepted Manuscript true © 2023 IEEE. Personal use of this material is permitted. true eng
title Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
spellingShingle Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Yuanbo Wu
title_short Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
title_full Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
title_fullStr Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
title_full_unstemmed Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
title_sort Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
author_id_str_mv 205b1ac5a767e977bebb5d6afd770784
author_id_fullname_str_mv 205b1ac5a767e977bebb5d6afd770784_***_Yuanbo Wu
author Yuanbo Wu
author2 Xu Yuan
Zheng Zhang
Xunguang Wang
Yuanbo Wu
format Journal article
container_title IEEE Transactions on Information Forensics and Security
container_volume 18
container_start_page 4681
publishDate 2023
institution Swansea University
issn 1556-6013
1556-6021
doi_str_mv 10.1109/tifs.2023.3297791
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerability. Notably, it is challenging to efficiently distill reliable semantic representatives for deep hashing to guide adversarial learning, and thereby it hinders the enhancement of adversarial robustness of deep hashing-based retrieval models. Moreover, current researches on adversarial training for deep hashing are hard to be formalized into a unified minimax structure. In this paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the adversarial robustness of deep hashing models. Specifically, we conceive a discriminative mainstay features learning (DMFL) scheme to construct semantic representatives for guiding adversarial learning in deep hashing. Particularly, our DMFL with the strict theoretical guarantee is adaptively optimized in a discriminative learning manner, where both discriminative and semantic properties are jointly considered. Moreover, adversarial examples are fabricated by maximizing the Hamming distance between the hash codes of adversarial samples and mainstay features, the efficacy of which is validated in the adversarial attack trials. Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes. Extensive experiments on benchmark datasets show superb attack performance against the state-of-the-art algorithms, meanwhile, the proposed adversarial training can effectively eliminate adversarial perturbations for trustworthy deep hashing-based retrieval. Our code is available at https://github.com/xandery-geek/SAAT.
published_date 2023-07-21T17:21:47Z
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score 11.08899

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