Adversarially Robust Deep Image Super-Resolution Using Entropy Regularization

Jun Ho Choi, Huan Zhang, Jun Hyuk Kim, Cho Jui Hsieh, Jong Seok Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationComputer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
EditorsHiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
Number of pages17
ISBN (Print)9783030695378
StatePublished - 2021
Externally publishedYes
Event15th Asian Conference on Computer Vision, ACCV 2020 - Virtual, Online
Duration: Nov 30 2020Dec 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12625 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Asian Conference on Computer Vision, ACCV 2020
CityVirtual, Online

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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