Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks

@article{Choi2019EvaluatingRO,
  title={Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks},
  author={J. Choi and Huan Zhang and J. Kim and Cho-Jui Hsieh and J. Lee},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={303-311}
}
  • J. Choi, Huan Zhang, +2 authors J. Lee
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many image processing applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly deteriorate the super-resolved images without noticeable distortion in the attacked low-resolution images. It is demonstrated that state-of-the-art deep super-resolution methods are… CONTINUE READING
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