Spectral Detection Adversary for confronting against spectral-based object detection

Spectral images provide rich spatial and spectral information, enabling quantitative analysis of the same material and qualitative analysis of different materials. Due to the outstanding material identification capabilities of spectral technology, it is widely used in high-precision target detection tasks in complex scenes. Currently, adversarial sample attack techniques are advancing rapidly, however, most research on adversarial sample attacks in the field of object detection has been focused on RGB three-channel images. The exploration of adversarial techniques for object detection in multi-channel spectral images is still in its early stages. In this work, we propose a method for adversarial sample generation based on spectral images, which belongs to black-box attack and targeted attack. The reported technique, named Spectral Detection Adversary (SDA), is utilized to cause spectral image object detection networks to misclassify camouflage targets as real targets. We introduce a spectral analysis and comparison method to distinguish between real targets and camouflage targets, additionally, we propose a spectral dimension encoding method for various categories of real and camouflaged targets, thereby causing disruption in the adversary's spectral image object detection network. In the most effective group, experimental verification revealed a reduction of more than 60% in the recall of camouflaged targets and a decrease of over 30% in the precision of real targets.