Adversarial camouflage for mobile targets against battlefield optical and infrared AI detection

Modern battlefield perception systems, which rely on multi-modal target detection technologies such as optical and infrared sensing, pose a serious threat to the survivability of large mobile assets. Existing adversarial camouflage research has predominantly focused on single-band (optical or infrared) reconnaissance, lacking a comprehensive solution capable of simultaneously deceiving both optical and infrared detectors while maintaining visual concealment. To address this gap, this study proposes a Multi-modal Adversarial Camouflage (MAC) patch framework. Firstly, the Scene-Adaptive Digital Camouflage Generation (SADCG) algorithm is designed to generate a digital camouflage base for tanks that exhibits high fusion with the background. Secondly, a joint loss optimization function is devised to train a YOLOv5-based patch generation model for producing optical adversarial patches. Simultaneously, a shape parameterization method utilizing multi-anchor representation and Catmull-Rom splines, along with a three-layer constrained boundary restriction system, is developed to optimize infrared shape adversarial patches. These optical and infrared patches are then integrated onto the camouflage base using Indium Tin Oxide (ITO) transparent thin films, forming a unified multi-modal stealth system. Experimental results demonstrate that the MAC framework can effectively reduce the mean Average Precision (mAP) of both optical and infrared detectors (including Faster R-CNN and YOLOv4) by over 75%, while significantly suppressing the response of infrared detectors. Furthermore, subjective evaluations confirm its excellent visual and infrared concealment properties. This research provides a verifiable solution and a technical pathway for achieving multi-spectral stealth for equipment in the physical world.