Generating Adversarial Remote Sensing Images via Pan-Sharpening Technique

Abstract

Pan-sharpening is one of the most commonly used techniques in remote sensing, which fuses panchromatic (PAN) and multispectral (MS) images to obtain both the high spectral and high spatial resolution images. Due to these advantages, researchers usually apply object detectors on these pan-sharpened images to achieve reliable detection results. However, recent studies have shown that deep learning-based object detection methods are vulnerable to adversarial examples, i.e., adding imperceptible noises on clean images can fool well-trained deep neural networks. It is interesting to combine the pan-sharpening technique and adversarial examples to attack object detectors in remote sensing. In this paper, we propose a method to generate adversarial pan-sharpened images. We utilize a generative network to generate the pan-sharpened images, and then propose the shape loss and label loss to perform the attack task. To guarantee the quality of pan-sharpened images, a perceptual loss is utilized to balance spectral preserving and attacking performance. The proposed method is applied to attack two object detectors: Faster R-CNN and Feature Pyramid Networks (FPN). Experimental results on GaoFen-1 satellite images demonstrate that the proposed method can generate effective adversarial images. The mAP of Faster R-CNN with VGG16 drops significantly from 0.870 to 0.014.