Computer Science > Computer Vision and Pattern Recognition
[Submitted on 20 Nov 2022 (v1), last revised 10 May 2023 (this version, v2)]
Title:Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
View PDFAbstract:In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one or all-to-all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. Thus, previous backdoor attack methods for classification are not applicable for ReID. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities. Specifically, an identity hashing network is proposed to first extract target identity information from a reference image, which is then injected into the benign images by image steganography. We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets, and evaluate the effectiveness of several defense methods against our attack.
Submission history
From: Wenli Sun [view email][v1] Sun, 20 Nov 2022 10:08:28 UTC (7,656 KB)
[v2] Wed, 10 May 2023 14:19:15 UTC (5,834 KB)
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