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Adversarial perturbation and defense for generalizable person re-identification.

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Title: Adversarial perturbation and defense for generalizable person re-identification.
Authors: Tan H; Institute of Future Technology, Dalian University of Technology, Dalian, Dalian 116024, China. Electronic address: tanhongchenphd@bjut.edu.cn., Xu K; School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China. Electronic address: 459553299@qq.com., Tao P; Shandong University, Weihai 264209, China. Electronic address: pingping.tao@sdu.edu.cn., Liu X; School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China. Electronic address: xpliu@dlut.edu.cn.
Source: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2025 Jun; Vol. 186, pp. 107287. Date of Electronic Publication: 2025 Feb 22.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s): Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms: Biometric Identification*/methods , Convolutional Neural Networks* , Deep Learning* , Semantics* , Detection Algorithms*, Software Validation
Abstract: Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
In the Domain Generalizable Person Re-Identification (DG Re-ID) task, the quality of identity-relevant descriptor is crucial for domain generalization performance. However, for hard-matching samples, it is difficult to separate high-quality identity-relevant feature from identity-irrelevant feature. It will inevitably affect the domain generalization performance. Thus, in this paper, we try to enhance the model's ability to separate identity-relevant feature from identity-irrelevant feature of hard matching samples, to achieve high-performance domain generalization. To this end, we propose an Adversarial Perturbation and Defense (APD) Re-identification Method. In the APD, to synthesize hard matching samples, we introduce a Metric-Perturbation Generation Network (MPG-Net) grounded in the concept of metric adversariality. In the MPG-Net, we try to perturb the metric relationship of samples in the latent space, while preserving the essential visual details of the original samples. Then, to capture high-quality identity-relevant feature, we propose a Semantic Purification Network (SP-Net). The hard matching samples synthesized by MPG-Net is used to train the SP-Net. In the SP-Net, we further design the Semantic Self-perturbation and Defense (SSD) Scheme, to better disentangle and purify identity-relevant feature from these hard matching samples. Above all, through extensive experimentation, we validate the effectiveness of the APD method in the DG Re-ID task.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)
Contributed Indexing: Keywords: Generalizable person Re-ID; Hard matching samples; Metric perturbation; Semantics purification
Entry Date(s): Date Created: 20250226 Date Completed: 20250319 Latest Revision: 20250319
Update Code: 20250319
DOI: 10.1016/j.neunet.2025.107287
PMID: 40010295
Database: MEDLINE
Description
Abstract:Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />In the Domain Generalizable Person Re-Identification (DG Re-ID) task, the quality of identity-relevant descriptor is crucial for domain generalization performance. However, for hard-matching samples, it is difficult to separate high-quality identity-relevant feature from identity-irrelevant feature. It will inevitably affect the domain generalization performance. Thus, in this paper, we try to enhance the model's ability to separate identity-relevant feature from identity-irrelevant feature of hard matching samples, to achieve high-performance domain generalization. To this end, we propose an Adversarial Perturbation and Defense (APD) Re-identification Method. In the APD, to synthesize hard matching samples, we introduce a Metric-Perturbation Generation Network (MPG-Net) grounded in the concept of metric adversariality. In the MPG-Net, we try to perturb the metric relationship of samples in the latent space, while preserving the essential visual details of the original samples. Then, to capture high-quality identity-relevant feature, we propose a Semantic Purification Network (SP-Net). The hard matching samples synthesized by MPG-Net is used to train the SP-Net. In the SP-Net, we further design the Semantic Self-perturbation and Defense (SSD) Scheme, to better disentangle and purify identity-relevant feature from these hard matching samples. Above all, through extensive experimentation, we validate the effectiveness of the APD method in the DG Re-ID task.<br /> (Copyright © 2025 Elsevier Ltd. All rights reserved.)
ISSN:1879-2782
DOI:10.1016/j.neunet.2025.107287