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arXiv · 2309.13763

Combining Two Adversarial Attacks Against Person Re-Identification Systems

Eduardo de O. Andrade·Igor Garcia Ballhausen Sampaio·Joris Guérin·José Viterbo
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Abstract

The field of Person Re-Identification (Re-ID) has received much attention recently, driven by the progress of deep neural networks, especially for image classification. The problem of Re-ID consists in identifying individuals through images captured by surveillance cameras in different scenarios. Governments and companies are investing a lot of time and money in Re-ID systems for use in public safety and identifying missing persons. However, several challenges remain for successfully implementing Re-ID, such as occlusions and light reflections in people's images. In this work, we focus on adversarial attacks on Re-ID systems, which can be a critical threat to the performance of these systems. In particular, we explore the combination of adversarial attacks against Re-ID models, trying to strengthen the decrease in the classification results. We conduct our experiments on three datasets: DukeMTMC-ReID, Market-1501, and CUHK03. We combine the use of two types of adversarial attacks, P-FGSM and Deep Mis-Ranking, applied to two popular Re-ID models: IDE (ResNet-50) and AlignedReID. The best result demonstrates a decrease of 3.36% in the Rank-10 metric for AlignedReID applied to CUHK03. We also try to use Dropout during the inference as a defense method.

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Eduardo de O. Andrade, Igor Garcia Ballhausen Sampaio, Joris Guérin, José Viterbo. 2023-09-24. Combining Two Adversarial Attacks Against Person Re-Identification Systems. https://doi.org/10.5220/0011623800003417

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