Object Detection Adversarial Attack for Infrared Imagery in Remote Sensing

Abstract

Aiming at the problems of poor effect of existing adversarial attack for object detection algorithms on small-scale target attack, a large number of meaningless disturbances in adversarial samples and low disturbance genera-tion efficiency, taking infrared remote sensing as the application background, a object detection adversarial attack algorithm with strong generalization is proposed based on the adversarial generation attack theory. The dilated convolution and attention mechanism are employed to construct multi-channel and various scale disturbance generation network to overcome the problem of small targets in infrared remote sensing images. Meanwhile, a filter is designed based on the heat map of detection result to filter the generated disturbance information and eliminate the meaningless disturbance. Finally, the data set published in the third "Aerospace Cup" national innovation and creativity competition is taken as an example for experimental analysis. Compared with the suboptimal attack algorithms, the average attack success rate of the proposed algorithm is increased by 0.313, and the average time of generating adversarial samples is reduced 57.409 s. In addition, using the generated adversarial samples to transfer and attack other types of detectors, the average detection accuracy of YOLOv3 detector, YOLOv5 detector and Faster-RCNN detector is reduced by 0.032, 0.287 and 0.09 respectively. Experimental results show that the proposed algorithm has significant advantages in the physical realizability, transferability and generation speed of adversarial samples.

Keywords

• |infrared imagery|adversarial attack for object detection|dim and small targetindex|adversarial generation attack|physical realizability|transferability|attack algorithm