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Robust Unpaired Single Image Super-Resolution of Faces

arXiv 2201.09109 ↗First posted 22 Jan 2022cs.CVcited by 0

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Abstract (arXiv)

We propose an adversarial attack for facial class-specific Single Image Super-Resolution (SISR) methods. Existing attacks, such as the Fast Gradient Sign Method (FGSM) or the Projected Gradient Descent (PGD) method, are either fast but ineffective, or effective but prohibitively slow on these networks. By closely inspecting the surface that the MSE loss, used to train such networks, traces under varying degradations, we were able to identify its parameterizable property. We leverage this property to propose an adverasrial attack that is able to locate the optimum degradation (effective) without needing multiple gradient-ascent steps (fast). Our experiments show that the proposed method is able to achieve a better speed vs effectiveness trade-off than the state-of-theart adversarial attacks, such as FGSM and PGD, for the task of unpaired facial as well as class-specific SISR.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 22 Jan 2022.

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References · 17

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  4. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  5. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  6. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  7. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  8. 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 624
  9. 2019Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Modelcited by 400
  10. 2015WIDER FACE: A Face Detection Benchmarkcited by 297
  11. 2019You Only Propagate Once: Accelerating Adversarial Training via Maximal Principlecited by 181
  12. 2018To learn image super-resolution, use a GAN to learn how to do image degradation firstcited by 94
5 more
  1. 2020Unpaired Image Super-Resolution using Pseudo-Supervisioncited by 67
  2. 2018Bringing Alive Blurred Momentscited by 44
  3. 2019Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attackscited by 37
  4. 2018Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Networkcited by 16
  5. 2015Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Netscited by 9

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