Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
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Deep neural networks (DNNs) have a wide range of applications in the field of image denoising, and they are superior to traditional image denoising. However, DNNs inevitably show vulnerability, which is the weak robustness in the face of adversarial attacks. In this paper, we find some similitudes between existing deep image denoising methods, as they are consistently fooled by adversarial attacks. First, denoising-PGD is proposed which is a denoising model full adversarial method. The current mainstream non-blind denoising models (DnCNN, FFDNet, ECNDNet, BRDNet), blind denoising models (DnCNN-B, Noise2Noise, RDDCNN-B, FAN), and plug-and-play (DPIR, CurvPnP) and unfolding denoising models (DeamNet) applied to grayscale and color images can be attacked by the same set of methods. Second, since the transferability of denoising-PGD is prominent in the image denoising task, we design experiments to explore the characteristic of the latent under the transferability. We correlate transferability with similitude and conclude that the deep image denoising models have high similitude. Third, we investigate the characteristic of the adversarial space and use adversarial training to complement the vulnerability of deep image denoising to adversarial attacks on image denoising. Finally, we constrain this adversarial attack method and propose the L2-denoising-PGD image denoising adversarial attack method that maintains the Gaussian distribution. Moreover, the model-driven image denoising BM3D shows some resistance in the face of adversarial attacks.
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