Adversarial Attacks On Aerial Vehicle Policies; Poster 2022

Deep Neural Networks are widely applied for solving Computer Vision tasks for Unmanned Aerial Vehicles (UAVs). For some applications, the predictions of the neural networks (NNs) directly influence the motion planning or control of the UAVs. However, the neural networks are highly prone to adversarial attacks, which has a severe negative impact on the drone’s safe operation. With this work, we are planning to perform a physically realizable attack on a neural network analyzing camera images. The control of the UAV is directly influenced by the predictions of this NN. The generated adversarial attacks will be printed and attached as adversarial patches to an attacker UAV. By choosing which patch to present given the current relative poses of victim and attacker, the attacker will achieve full control over the victim UAV.