Quad-Rotor Helicopter Visual Attack Based on Attention Mechanism Grad-CAM++ and Mask-PGD

In order to explore security vulnerabilities in unmanned aerial vehicle (UAV) target tracking processes, a quad-rotor helicopter visual adversarial sample attack method based on Grad-CAM++ and Mask-PGD is proposed in this article. Initially, an attention mechanism is employed to select image frames from the video stream that significantly enhance the attack’s effectiveness, using Grad-CAM++ to identify target-sensitive regions. Then, the target heatmap is overlaid as a mask onto the noise map generated by projected gradient descent (PGD), creating perturbed samples, which are subsequently fused with the original image frames to generate adversarial samples. Finally, these adversarial samples are injected into the UAV’s video stream data, rendering the UAV incapable of detecting targets and disrupting its normal tracking operations. Compared to the PGD method, the proposed approach achieves improvements of 69.39%, 64.01%, and 44.55% in terms of $L_{2}$ norm, structural similarity index, and peak signal-to-noise ratio, respectively. In experimental visual attacks on the quad-rotor helicopter Tello, processing only a small portion of key image frames from the video stream is sufficient to disrupt Tello’s tracking tasks.