AGS: Transferable adversarial attack for person re-identification by adaptive gradient similarity attack

Person re-identification (Re-ID) has achieved tremendous success in the fields of computer vision and security. However, Re-ID models are susceptible to adversarial examples, which are crafted by introducing imperceptible perturbations to benign person images. These adversarial examples often display high success rates in white-box settings but their transferability to black-box settings is relatively low. To improve the transferability of adversarial examples, this paper proposes a novel approach called the adaptive gradient similarity attack (AGS), which encompasses two essential components: gradient similarity and enhanced second moment. Specifically, a gradient similarity modulation is established to better harness the information in the neighborhood of the adjacent input, which can adaptively correct the update direction. Additionally, this paper formulates an enhanced second moment to adjust the update step at each iteration to address the issue of poor transferability. Extensive experiments confirm that AGS achieves the best performance compared with the state-of-the-art gradient-based attacks. Moreover, AGS is a versatile approach that can be integrated with existing input transformation attack techniques. Code is available at https://github.com/ZezeTao/similar_Attack4.