Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

Huiming Sun, Lan Fu, Jinlong Li, Qing Guo, Zibo Meng, Tianyun Zhang, Yuewei Lin, Hongkai Yu

Abstract: Detecting the salient objects in a remote sensing image has wide applications. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images with remarkable results. However, the recent adversarial attack examples, generated by changing a few pixel values on the original image, could result in a collapse for the well-trained deep learning model. Different with existing methods adding perturbation to original images, we propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. Cloud is natural and common in remote sensing images, however, camouflaging cloud based adversarial attack and defense for remote sensing images are not well studied before. Furthermore, we design DefenseNet as a learnable pre-processing to the adversarial cloudy images to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model. By considering both regular and generalized adversarial examples, the proposed DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting. Experimental results on a synthesized benchmark from the public remote sensing dataset (EORSSD) show the promising defense against adversarial cloud attacks.