Upcycling adversarial attacks for infrared object detection

Abstract

Recently, infrared object detection (IOD) has been extensively studied due to the rapid growth of deep neural networks (DNNs). An adversarial attack using imperceptible perturbation can dramatically deteriorate the performance of DNNs. Most of the existing adversarial attacks are focused on visible image recognition (VIR), but there are few attacks for IOD. Moreover, the existing attacks are challenging to exploit for state-of-the-art detectors (e.g., EfficientDet) due to low compatibility. To solve the problem, we propose a novel upcycling adversarial attack for IOD by expanding the highly compatible adversarial attacks for the VIR task. We also propose a novel evaluation metric, attack efficiency (AE), to compare the effectiveness of different adversarial attacks. Since the AE value increases with the small perturbation size and the significant performance drop, we can concurrently compare the similarity and performance degradation between adversarial and clean images for various attacks. We validate our approaches through comprehensive experiments on two challenging datasets (e.g., FLIR and MSOD) for the infrared domain.