Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
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Neural networks are vulnerable to adversarial examples. This phenomenon poses a threat to their applications in security-sensitive systems. It is thus important to develop effective defending methods to strengthen the robustness of neural networks to adversarial attacks. Many techniques have been proposed, but only a few of them are validated on large datasets like the ImageNet dataset. We propose high-level representation guided denoiser (HGD) as a defense for image classification. HGD uses a U-net structure to capture multi-scale information. It serves as a preprocessing step to remove the adversarial noise from the input, and feeds its output to the target model. To train the HGD, we define the loss function as the difference of the target model's outputs activated by the clean image and denoised image. Compared with the traditional denoiser that imposes loss function at the pixel-level, HGD is better at suppressing the influence of adversarial noise. Compared with ensemble adversarial training which is the state-of-the-art defending method, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images, which makes the training much easier on large-scale datasets. Third, HGD can be transferred to defend models other than the one guiding it. We further validated the proposed method in NIPS adversarial examples dataset and achieved state-of-the-art result.
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