Dynamic adversarial patch for evading object detection models

Shahar Hoory, Tzvika Shapira, Asaf Shabtai, Yuval Elovici

arXiv preprint arXiv:2010.13070, 2020

Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real-world adversarial attacks against object detectors use an adversarial patch which is attached to the target object (e.g., a carefully crafted sticker placed on a stop sign). This method may not be robust to changes in the camera’s location relative to the target object; in addition, it may not work well when applied to nonplanar objects such as cars. In this study, we present an innovative attack method against object detectors applied in a real-world setup that addresses some of the limitations of existing attacks. Our method uses dynamic adversarial patches which are placed at multiple predetermined locations on a target object. An adversarial learning algorithm is applied in order to generate the patches used. The dynamic attack is implemented by switching between optimized patches dynamically, according to the camera’s position (i.e., the object detection system’s position). In order to demonstrate our attack in a real-world setup, we implemented the patches by attaching flat screens to the target object; the screens are used to present the patches and switch between them, depending on the current camera location. Thus, the attack is dynamic and adjusts itself to the situation to achieve optimal results. We evaluated our dynamic patch approach by attacking the YOLOv2 object detector with a car as the target object and succeeded in misleading it in up to 90% of the video frames when filming the car from a wide viewing angle range. We improved the attack by generating …