Increasing Neural-Based Pedestrian Detector's Robustness to Adversarial Patch Attacks Using Anomaly Localization

Object detection in images is a fundamental component of many safety-critical systems, such as autonomous driving, video surveillance systems, and robotics. Adversarial patch attacks, which are easily implemented in the real world, provide effective counteraction to the object detection by state-of-the-art neural-based detectors. It poses a serious danger in various fields of activity. Existing defense methods against patch attacks are insufficiently effective, which underlines the need to develop new reliable solutions. In this manuscript, a new approach is proposed to increase the robustness of neural network systems to the input adversarial images. The proposed method, based on the anomaly localization, demonstrates high resistance to adversarial patch attacks while maintaining the high quality of the object detection. The results of the study demonstrate that the proposed method is promising for security of object detection systems improvement and threat of adversarial patch attacks counteraction.