  Object detection techniques for Unmanned Aerial Vehicles (UAVs) rely on Deep
Neural Networks (DNNs), which are vulnerable to adversarial attacks.
Nonetheless, adversarial patches generated by existing algorithms in the UAV
domain pay very little attention to the naturalness of adversarial patches.
Moreover, imposing constraints directly on adversarial patches makes it
difficult to generate patches that appear natural to the human eye while
ensuring a high attack success rate. We notice that patches are natural looking
when their overall color is consistent with the environment. Therefore, we
propose a new method named Environmental Matching Attack(EMA) to address the
issue of optimizing the adversarial patch under the constraints of color. To
the best of our knowledge, this paper is the first to consider natural patches
in the domain of UAVs. The EMA method exploits strong prior knowledge of a
pretrained stable diffusion to guide the optimization direction of the
adversarial patch, where the text guidance can restrict the color of the patch.
To better match the environment, the contrast and brightness of the patch are
appropriately adjusted. Instead of optimizing the adversarial patch itself, we
optimize an adversarial perturbation patch which initializes to zero so that
the model can better trade off attacking performance and naturalness.
Experiments conducted on the DroneVehicle and Carpk datasets have shown that
our work can reach nearly the same attack performance in the digital attack(no
greater than 2 in mAP$\%$), surpass the baseline method in the physical
specific scenarios, and exhibit a significant advantage in terms of naturalness
in visualization and color difference with the environment.
