The main challenges of ReID is the intra-class variations caused by color
deviation under different camera conditions. Simultaneously, we find that most
of the existing adversarial metric attacks are realized by interfering with the
color characteristics of the sample. Based on this observation, we first
propose a local transformation attack (LTA) based on color variation. It uses
more obvious color variation to randomly disturb the color of the retrieved
image, rather than adding random noise. Experiments show that the performance
of the proposed LTA method is better than the advanced attack methods.
Furthermore, considering that the contour feature is the main factor of the
robustness of adversarial training, and the color feature will directly affect
the success rate of attack. Therefore, we further propose joint adversarial
defense (JAD) method, which include proactive defense and passive defense.
Proactive defense fuse multi-modality images to enhance the contour feature and
color feature, and considers local homomorphic transformation to solve the
over-fitting problem. Passive defense exploits the invariance of contour
feature during image scaling to mitigate the adversarial disturbance on contour
feature. Finally, a series of experimental results show that the proposed joint
adversarial defense method is more competitive than a state-of-the-art methods.

该论文提出了一种基于颜色变异的局部变换攻击方法以及包括主动防御和被动防御的联合对抗防御方法，通过一系列实验结果表明这些新方法比其他先进的攻击和防御方法更具竞争力。