Adv-Camou: An Emissivity-based Physical Dynamic Adversarial Patch for Infrared Attack

Abstract

With the widespread application of infrared object detection technologies in military domains, intelligent infrared adversarial camouflage capabilities have become critical. Existing physical attack methods predominantly employ static thermal suppression strategies, suffering from limited environmental adaptability, insufficient attack robustness, and inadequate visual concealment. This study proposes Adv-Camou, an emissivity-based physical dynamic adversarial patch that achieves paradigm-shifting advancements from static to dynamic attacks through algorithm-material co-innovation. First, a multi-level loss function based on feature confusion mechanisms is designed to deceive detectors by attenuating target saliency and enforcing background feature alignment. Second, adaptive physical transformations and differentiable binarization optimization are integrated to address physical robustness and realizability constraints. A 6*4 array of reversible electrochromic emissivity device is developed and tested across a range of background scenarios, enabling scene-adaptive dynamic camouflage through independent unit control. Experiments demonstrate that this method achieves 95.59% attack success rate (ASR), reducing average precision (AP) by 60.51%. Physical validations show that static adversarial patches attain 76.55% average ASR under multi-view, multi-range, and multi-spectral conditions, while dynamic patches enhance ASR up to 91.55% in complex environments (wilderness/urban) through adaptive switching. The seamless digital-to-physical transfer via tunable emissivity devices transcends the limitations of traditional static camouflage by establishing a dynamic infrared regulation capability. This advancement enhances the battlefield survivability of military assets and demonstrates transformative potential for adaptive camouflage in practical combat scenarios.