Paa-Tee: A Practical Adversarial Attack on Thermal Infrared Detectors with Temperature and Pose Adaptability

Thermal infrared object detectors play an important role in security-related tasks, necessitating feasible adversarial attacks to evaluate their robustness. In many cases, implementing attacks in the physical space by a patch demands intricate and specialized perturbations. However, state-of-the-art adversarial attacks are often impractical, as they require fixed perturbation location and are susceptible to environmental temperature, leading to attack effects overfitting to specific poses and environments. To address this, we propose a practical adversarial attack method named Paa-Tee, with two input transformation strategies. For poses, we continuously alter the patch’s position to mitigate the impact of different poses on the patch’s location. For temperature, leveraging the principles of thermal imaging, we apply various transformations to a single input image to simulate different attack environments. Meanwhile, we utilize hot and cold pastes as low-resolution patches to implement attacks in the physical world. Extensive experiments validate the efficacy of our approach in both the digital and physical worlds. In the digital world, our attacks reduce the average precision of mainstream detectors by 65.44%. In the physical world, we achieve an average attack success rate of 63.77% under various distances, poses, angles, and environmental conditions.