Towards Robust Person Re-Identification by Adversarial Training with Dynamic Attack Strategy

Recently, person re-identification has gained significant attention from both academic and industry fields due to its potential applications in surveillance and security. However, the security of re-identification systems has not been widely investigated, and they are vulnerable to adversarial attacks, which can significantly degrade their performance. Although numerous sophisticated adversarial training methods have been proposed for image classification, metric analysis systems such as person re-identification have not been fully explored. In this paper, we develop a novel adversarial training framework with a dynamic attack strategy for person re-identification, to further enhance the robustness of the model. Specifically, we gradually increase the perturbation budget during the generation until the generated adversarial examples reach a certain level of attack strength. As the iterations progress, the model becomes more robust, and our framework can generate stronger adversarial examples to continuously explore the robustness bounds of the model. Moreover, to alleviate the conflict between the adversarial robustness and natural generalization of the model, we design a novel performance alignment loss to further constrain the adversarial example generation process, which can make the generated adversarial examples as close as possible to the clean samples in terms of performance. Experiments on two widely used person re-ID benchmark datasets demonstrate the effectiveness and superiority of our proposed method.