Towards a Robust Adversarial Patch Attack Against Unmanned Aerial Vehicles Object Detection

Object detection techniques for autonomous Un-manned Aerial Vehicles (UAV) are built upon Deep Neural Networks (DNN), which are known to be vulnerable to adversarial patch perturbation attacks that lead to object detection evasion. Yet, current adversarial patch generation schemes are not designed for UAV imagery settings. This paper proposes a new robust adversarial patch generation attack against object detection with UAVs. We build adversarial patches considering UAV-specific settings such as the UAV camera perspective, viewing angle, distance, and brightness changes. As a result, built patches can also degrade the accuracy of object detector models implemented with different initializations and architectures. Experiments conducted on the VisDrone dataset have shown the proposal's feasibility, achieving an attack success rate of up to 80% in a white-box setting. In addition, we also transfer the patch against DNN models with different initializations and different architectures, reaching attack success rates of up to 75% and 78%, respectively, in a gray-box setting. GitHub: https://github.com/SamSamhuns/yolov5_adversarial