TY - GEN
T1 - Adversarially Robust Deep Image Super-Resolution Using Entropy Regularization
AU - Choi, Jun Ho
AU - Zhang, Huan
AU - Kim, Jun Hyuk
AU - Hsieh, Cho Jui
AU - Lee, Jong Seok
N1 - Funding Information:
Acknowledgement. This work was supported by the NRF grant funded by the Korea government (MSIT) (NRF-2020R1F1A1070631), and the Artificial Intelligence Graduate School Program (Yonsei University, 2020-0-01361).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85103279124&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-69538-5_19
DO - 10.1007/978-3-030-69538-5_19
M3 - Conference contribution
AN - SCOPUS:85103279124
SN - 9783030695378
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 301
EP - 317
BT - Computer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
A2 - Ishikawa, Hiroshi
A2 - Liu, Cheng-Lin
A2 - Pajdla, Tomas
A2 - Shi, Jianbo
PB - Springer
T2 - 15th Asian Conference on Computer Vision, ACCV 2020
Y2 - 30 November 2020 through 4 December 2020
ER -