Adversarial Patch Camouflage against Aerial Detection

Rust-Style Patch: A Physical and Naturalistic Camouflage Attacks on Object Detector for Remote Sensing Images

“…We can hide some targets that we do not want to be discovered by disappearing attack, such as the military, traditional methods often use camouflage nets to hide military assets, but for some large items such as airplanes, cars, etc., it is difficult to use traditional camouflage to conceal, so disappearance attack is always an efficient way for us to find a patch perturbation method so that we can hide our large assets in remote sensing images without being detected. In [21], it is found that an adversarial patch block can be generated and pasted on the target to achieve the purpose of not being detected by the object detector, which proves that the adversarial attack can be applied to the remote sensing system. However, in the real scene, the background of remote sensing images is diverse and complex, and there are some challenging physical conditions such as distance and angle which make attacks more difficult.…”

“…An improvement of adversarial-yolo is proposed in [36], the method input the optimized latent variables into the pre-trained GAN network in each iteration, and finally generated an adversarial pattern similar to the real thing (such as Dog), and experiments on multiple object detectors demonstrate the robustness of the attack. In [21], the author extended the method in adversarial-yolo to military remote sensing object detection and realized the attack of hidden targets by adding perturbation patches on different types of aircraft fuselages. There are two kinds of adversarial patches proposed in [37] to fool the aerial imagery object detector, patches were installed on or near target objects to significantly reduce the efficacy of an object detector applied on overhead images.…”