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Adversarial pan-sharpening attacks for object detection in remote sensing.
- Source :
-
Pattern Recognition . Jul2023, Vol. 139, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- • CNN based detectors are vulnerable to adversarial pan-sharpening images. • Pan-sharpening technology is used to generate adversarial remote sensing images. • We propose a pan-sharpening network for attacking object detector. Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to fuse texture-rich PAN images and multi-spectral MS images to obtain texture-rich MS images. With the development of deep learning, CNN based Pan-sharpening methods have received more and more attention in recent years. Since Pan-sharpening technique can integrate the complementary information of Pan and MS images, 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 noise to clean images can fool well-trained deep neural networks. It is interesting to combine the pan-sharpening technique with adversarial examples to attack object detectors in remote sensing. In this paper, we propose a framework to generate adversarial pan-sharpened images. Specifically, we propose a two-stream network to generate the pan-sharpened images, and then utilize 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 preservation and attacking performance. Experimental results demonstrate that the proposed method can generate effective adversarial pan-sharpened images that maintain a high success rate for white-box attacks and achieve transferability for black-box attacks. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 139
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 162848503
- Full Text :
- https://doi.org/10.1016/j.patcog.2023.109466