  The security of the Person Re-identification(ReID) model plays a decisive
role in the application of ReID. However, deep neural networks have been shown
to be vulnerable, and adding undetectable adversarial perturbations to clean
images can trick deep neural networks that perform well in clean images. We
propose a ReID multi-modal data augmentation method with adversarial defense
effect: 1) Grayscale Patch Replacement, it consists of Local Grayscale Patch
Replacement(LGPR) and Global Grayscale Patch Replacement(GGPR). This method can
not only improve the accuracy of the model, but also help the model defend
against adversarial examples; 2) Multi-Modal Defense, it integrates three
homogeneous modal images of visible, grayscale and sketch, and further
strengthens the defense ability of the model. These methods fuse different
modalities of homogeneous images to enrich the input sample variety, the
variaty of samples will reduce the over-fitting of the ReID model to color
variations and make the adversarial space of the dataset that the attack method
can find difficult to align, thus the accuracy of model is improved, and the
attack effect is greatly reduced. The more modal homogeneous images are fused,
the stronger the defense capabilities is . The proposed method performs well on
multiple datasets, and successfully defends the attack of MS-SSIM proposed by
CVPR2020 against ReID [10], and increases the accuracy by 467 times(0.2% to
93.3%).The code is available at
https://github.com/finger-monkey/ReID_Adversarial_Defense.
