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.

Recent advancements in networking, embedded systems, mobile computing, and artificial intelligence (AI) have brought renewed focus on code-size reduction.And within this domain, the sequence of optimization passes, known as phaseordering, considered as a critical determinant.While traditional methods often fall short in addressing complex optimization landscapes, reinforcement learning (RL) algorithms have evolved as a formidable tools to tackle these challenges.According to existing research, previously applied optimization passes can significantly influence subsequent decisions-highlighting a gap in traditional RL algorithms' ability to account for temporal dynamics inherent to the phase-ordering problem.In response to this, our paper proposes a strategy employing Long Short-Term Memory (LSTM) in conjunction with the Proximal Policy Optimization (PPO) RL algorithm, leveraging the temporal relationships between optimization passes to discern an enhanced phase-ordering scheme.The experimental results shows that our model reaches a 3000 iterations fewer in convergence and an additional 5.758% reduction in code size in entire benchmark than PPO alone.Furthermore, our approach also demonstrates robust generalization capabilities and superior performance across various benchmarks and RL algorithms.

Physical adversarial attacks in aerial object detection have gained significant attention. Existing adversarial patches exhibit subpar visual effects and encounter limitations when transitioning from digital to physical spaces, restricting applicability in real-world scenarios. To address these challenges, we propose an adversarial texture generation method based on background texture design. This method selectively covers the background environment without interfering with the target surface. We also explore the translational invariance of fully convolutional networks to decouple adversarial textures from shapes, allowing adversarial textures to be arbitrarily expanded during use. The areas where adversarial textures are placed are designated as the “detection failure zone,” rendering the detector ineffective regardless of the aircraft’s position within this zone. This significantly enhances the practicality of the adversarial texture. To improve its concealment, we align the features of the adversarial textures with those of the original image using a pretrained VGG network, ensuring a consistent style and color tone with the background environment. Additionally, we employ a discriminator to further control the visual effects of the adversarial samples, ensuring effective concealment. Furthermore, we simulate the real environment in digital space using operations like affine transformations and Gaussian blur to transfer adversarial textures seamlessly from digital to physical space. This allows for the integration of adversarial textures into real environments without compromising their effectiveness. Experimental results demonstrate the effectiveness and consistent styling of the proposed adversarial texture in real-world environments, showing robustness against environmental changes, weather conditions, and viewing angles.