Adversarially Robust Deep Image Super-Resolution using Entropy Regularization

Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Abstract


Image super-resolution has been widely employed in various applications with boosted performance thanks to the deep learning techniques. However, many deep learning-based models are highly vulnerable to adversarial attacks, which is also applied to super-resolution models in recent studies. In this paper, we propose a defense method that is formulated as an entropy regularization loss for model training, which can be augmented to the original training loss of super-resolution models. We show that various state-of-the-art super-resolution models trained with our defense method are more robust against adversarial attacks than their original versions. To the best of our knowledge, this is the first attempt of adversarial defense for deep super-resolution models.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Choi_2020_ACCV, author = {Choi, Jun-Ho and Zhang, Huan and Kim, Jun-Hyuk and Hsieh, Cho-Jui and Lee, Jong-Seok}, title = {Adversarially Robust Deep Image Super-Resolution using Entropy Regularization}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} }