Adversarial perturbation and defense for generalizable person re-identification

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.