Part-Based Feature Squeezing To Detect Adversarial Examples in Person Re-Identification Networks

Although deep neural networks (DNNs) have achieved top performances in different computer vision tasks, such as object detection, image segmentation and person re-identification (ReID), they can easily be deceived by adversarial examples, which are carefully crafted images with perturbations that are imperceptible to human eyes. Such adversarial examples can significantly degrade the performance of existing DNNs. There are also targeted attacks misleading classifiers into making specific decisions based on attackers’ intentions. In this paper, we propose a new method to effectively detect adversarial examples presented to a person ReID network. The proposed method utilizes parts-based feature squeezing to detect the adversarial examples. We apply two types of squeezing to segmented body parts to better detect adversarial examples. We perform extensive experiments over three major datasets with different attacks, and compare the detection performance of the proposed body part-based approach with a ReID method that is not parts-based. Experimental results show that the proposed method can effectively detect the adversarial examples, and has the potential to avoid significant decreases in person ReID performance caused by adversarial examples.