RobusTReID: Defending Vision Transformer for Robust Image ReID

Vision Transformer (ViT) achieves competitive results in person ReID, not only due to the powerful ability on feature representation, but also its resistance to attacks. However, they are vulnerable to specially designed attacks. To enhance their robustness, this paper proposes RobusTReID to defend the ViT-based model against perturbed images without obvious performance drop on clean data. The basic idea is to incorporate the adversarial co-training into ReID, which first disturbs pixels by minimizing adversarial loss in primary feature branch, then optimizes model by ReID task loss computed in all branches on clean and perturbed data. We separate the representation paths for clean and perturbed images. Particularly, a learnable [ADV] token and low-rank positional embeddings (PE) are incorporated to build the feature for perturbed image. Extensive experiments on several ReID datasets show that our method effectively increases the robustness of ReID model under different types of attacks.