Physical Adversarial Camouflage Generation in Optical Remote Sensing Images

Physical adversarial examples in optical remote sensing have garnered significant attention in recent years due to their practicality and high adversarial threat potential. However, existing methods focus on position-fixed adversarial patches, neglecting tailored considerations for the domain-specific texture patterns and mobility required by aerial platforms. To address the issues above, we proposed a novel method of physical adversarial camouflage generation for the first time in optical remote sensing, which paints adversarial camouflage with specialized textures onto the targets to escape detection from DNN-based models. In pursuit of achieving a synthesis of visual harmony and adversarial attack potency, we propose a “latent variable-based” adversarial camouflage generation approach, in which we introduce a texture generator controlled by a group of latent variables to generate camouflage patterns with adversarial properties. By employing this idea, we can constrain the searching domain for adversarial examples to the domain characterized by camouflage exhibiting textures with high visual harmony, and easily focus on finding the most threatening ones during the optimization. We chose airplanes as the object of interest and object detection as the typical reconnaissance method in experiments. Our method achieved high attack success rates (ASRs) against a majority of existing detection models. Comparison with existing pixel-level optimization methods confirmed that the integration of a dedicated generator helps solve the trade-off dilemma between visual harmony and adversarial potency. Real-world experiments involving targets painted by our developed adversarial camouflage confirmed the adversarial attack potency and practicality, with a more than 50% increase on average in the ASRs compared to the conventional camouflage.