Generating Adversarial Remote Sensing Images via Pan-Sharpening Technique

Abstract

Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multi-spectral images to obtain high-resolution MS images. Since deep learning has received widespread attention because of its powerful fitting ability and efficient feature extraction, a variety of pan-sharpening methods have been proposed to achieve remarkable performance. However, current pan-sharpening methods usually require the paired PAN and MS images as the input, which limits their usage in some scenarios. To address this issue, in this paper, we observe that the spatial details from PAN images are mainly high-frequency cues, i.e., the edges reflect the contour of input PAN images. This motivates us to develop a PAN-agnostic representation to store some base edges, so as to compose the contour for the corresponding PAN image via them. As a result, we can perform the pan-sharpening task with only the MS image when inference. To this end, a memory-based network is adapted to extract and memorize the spatial details during the training phase and is used to replace the process of obtaining spatial information from PAN images when inference, which is called Memory-based Spatial Details Network (MSDN). We finally integrate the proposed MSDN module into the existing DL-based pan-sharpening methods to achieve an end-to-end pan-sharpening network. With extensive experiments on the Gaofen1 and WorldView-4 satellites, we verify that our method constructs good spatial details without PAN images and achieves the best performance. The code is available at https://github.com/Zhao-Tian-yi/Learning-to-Pan-sharpeningwith-Memories-of-Spatial-Details.git.

Benchmarking Adversarial Patch Against Aerial Detection

“…8, gradient-based adversarial attack methods aim to generate adversarial perturbations that are farthest from the decision boundary within the specified perturbation range. On the other hand, optimizationbased methods aim to minimize the size of the adversarial perturbation, i.e., the distance between the adversarial and (a) Pixel [16] (b) Watermark [84] (c) Trigger [85] (d) Patch [86] (e) Viewpoint [87] (f) Style [88] (g) Erosion [89] (h) Sticker [72] (i) Light [90] (j) Laser [91] (k) Color [92] (l) Zoom [93] (m) Texture [94] (n) 3D object [95] (o) Projection [96] (p) Makeup [97] (q) PS [98] (r) Location [99] Fig. 6: Adversarial perturbations in different forms.…”

“…Specifically, they employ an optimization process to create an adversarial pattern that, when overlaid onto a cloudless scene, causes the DNNs to falsely detect clouds in the image. In paper [98], the authors devise a novel approach for generating adversarial pan-sharpened images. To achieve this, a generative network is employed to generate the pan-sharpened images, followed by the application of shape and label loss to carry out the attack task.…”

“…Finally, we summarize adversarial attacks against object detection in RS ( [18], [73]- [75], [98], [249]- [258]) in Table X.…”