CNN-GCN Joint Network for Remote Sensing Scene Classification

Xu, Kejie; Hong, Huixiao; Deng, Peifang

doi:10.1109/igarss47720.2021.9553529

Cited by 3 publications

(1 citation statement)

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“…Moreover, Liao et al [ 40 ] designed a denoising method based on high-level representation guidance. Xu et al [ 41 ] proposed a denoising network that is guided by the scene classification task that transforms adversarial examples into images that are similar to the corresponding clean data based on the feedback from a target model. It has good performance with respect to removing adversarial noise and improving model robustness.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification

Da,

Zhang,

Wang

et al. 2023

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Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks.

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Section: Related Workmentioning

confidence: 99%