Investigation of the Robustness and Transferability of Adversarial Patches in Multi-View Infrared Target Detection.

This paper proposes a novel adversarial patch-generation method for infrared images, focusing on enhancing the robustness and transferability of infrared adversarial patches. To improve the flexibility and diversity of the generation process, a Bernoulli random dropout strategy is adopted. The loss function integrates multiple components, including target hiding loss, smoothing loss, structural similarity loss, and patch pixel value loss, ensuring that the generated patches maintain low texture complexity and natural visual features. During model training, the Grad-CAM algorithm is employed to identify the critical regions of interest in the target detector, where adversarial patches are applied to maximize the attack effectiveness. Furthermore, affine transformations and random erasing operations are introduced to increase the diversity and adaptability of patches, thereby enhancing their effectiveness across different scenarios. Experimental results demonstrate that the proposed GADP (Generative Adversarial Patch based on Bernoulli Random Dropout and Loss Function Optimization) algorithm achieves a high Attack Success Rate of 75.8% on various target detection models, significantly reducing the average precision (AP). Specifically, the AP of the YOLOv5s model drops from 81.3% to 15.1%. Compared with existing adversarial attack methods such as advYOLO Patch and QR Attack, GADP exhibits superior transferability and attack performance, reducing the Average Precision of multiple detection models to around 40%. The proposed method is not only theoretically innovative but also shows potential practical value, particularly in tasks such as unmanned aerial vehicle (UAV) detection and ground security under low-visibility environments. This study provides new insights into adversarial attack research for infrared target recognition.