mlsanityData to 7 Oct 2026

Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks

arXiv 1904.06097 ↗First posted 12 Apr 2019cs.CVcited by 37

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Abstract (arXiv)

Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision 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 highly vulnerable to adversarial attacks. Different levels of robustness of different methods are analyzed theoretically and experimentally. We also present analysis on transferability of attacks, and feasibility of targeted attacks and universal attacks.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2019.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 7; 2024: 6; 2025: 4; 2026: 1.

37 papers.

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  1. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
  2. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  3. 2022Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyondcited by 48
  4. 2021Fast Certified Robust Training with Short Warmupcited by 45
  5. 2022Imaging through the Atmosphere using Turbulence Mitigation Transformercited by 31
  6. 2021Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuningcited by 24
  7. 2023Physics-Driven Turbulence Image Restoration with Stochastic Refinementcited by 24
  8. 2022CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streamingcited by 23
  9. 2022Learning to Immunize Images for Tamper Localization and Self-Recoverycited by 19
  10. 2021Center Smoothing: Certified Robustness for Networks with Structured Outputscited by 18
  11. 2022Towards Adversarially Robust Deep Image Denoisingcited by 13
  12. 2022Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Qualitycited by 13
25 more
  1. 2021From Image to Imuge: Immunized Image Generationcited by 11
  2. 2022Robust Real-World Image Super-Resolution against Adversarial Attackscited by 11
  3. 2023On the unreasonable vulnerability of transformers for image restoration -- and an easy fixcited by 11
  4. 2021Generalized Real-World Super-Resolution through Adversarial Robustnesscited by 9
  5. 2021Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attackscited by 8
  6. 2021RAILS: A Robust Adversarial Immune-inspired Learning Systemcited by 8
  7. 2023Learning Provably Robust Estimators for Inverse Problems via Jitteringcited by 8
  8. 2022On Adversarial Robustness of Deep Image Deblurringcited by 7
  9. 2023The Perception-Robustness Tradeoff in Deterministic Image Restorationcited by 7
  10. 2024Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attackscited by 6
  11. 2023Adversarial Robustness in Unsupervised Machine Learning: A Systematic Reviewcited by 3
  12. 2025Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolutioncited by 3
  13. 2024Perceptual Fairness in Image Restorationcited by 2
  14. 2023Inter-frame Accelerate Attack against Video Interpolation Modelscited by 1
  15. 2024Evaluating Adversarial Robustness of Low dose CT Recoverycited by 1
  16. 2024Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overviewcited by 1
  17. 2024Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolutioncited by 1
  18. 2025Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Datasetcited by 1
  19. 2025BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolutioncited by 1
  20. 2021Kernel Adversarial Learning for Real-world Image Super-resolutioncited by 0
  21. 2022Robust Unpaired Single Image Super-Resolution of Facescited by 0
  22. 2023When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learningcited by 0
  23. 2024Adversarial Purification and Fine-tuning for Robust UDC Image Restorationcited by 0
  24. 2025On Inverse Problems, Parameter Estimation, and Domain Generalizationcited by 0
  25. 2026RefSR-Adv: Adversarial Attack on Reference-based Image Super-Resolution Modelscited by 0