mlsanityData to 13 Oct 2026

IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics

arXiv 2403.05955 ↗First posted 9 Mar 2024eess.IV cs.CVcited by 4

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Abstract (arXiv)

No-reference image- and video-quality metrics are widely used in video processing benchmarks. The robustness of learning-based metrics under video attacks has not been widely studied. In addition to having success, attacks that can be employed in video processing benchmarks must be fast and imperceptible. This paper introduces an Invisible One-Iteration (IOI) adversarial attack on no reference image and video quality metrics. We compared our method alongside eight prior approaches using image and video datasets via objective and subjective tests. Our method exhibited superior visual quality across various attacked metric architectures while maintaining comparable attack success and speed. We made the code available on GitHub: https://github.com/katiashh/ioi-attack.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 30 of its 56 references that are in the map. First posted on arXiv 9 Mar 2024.

Builds on

30 of 56 references.

References · 30

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,082
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,336
  3. 2013Intriguing properties of neural networkscited by 5,137
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,516
  5. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,687
  6. 2016Adversarial examples in the physical worldcited by 1,767
  7. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 684
  8. 2017One pixel attack for fooling deep neural networkscited by 581
  9. 2017NIMA: Neural Image Assessmentcited by 389
  10. 2019From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Qualitycited by 222
  11. 2021No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistencycited by 166
  12. 2016Learning a No-Reference Quality Metric for Single-Image Super-Resolutioncited by 143
18 more
  1. 2022Frequency Domain Model Augmentation for Adversarial Attackcited by 130
  2. 2019Sparse and Imperceivable Adversarial Attackscited by 95
  3. 2022Frequency-driven Imperceptible Adversarial Attack on Semantic Similaritycited by 77
  4. 2020Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Trainingcited by 72
  5. 2018On the Suitability of Lp-norms for Creating and Preventing Adversarial Examplescited by 58
  6. 2022Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loopcited by 30
  7. 2022Video compression dataset and benchmark of learning-based video-quality metricscited by 27
  8. 2022Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metricscited by 17
  9. 2019Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classificationcited by 12
  10. 2023Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attackscited by 12
  11. 2018Defending against Adversarial Images using Basis Functions Transformationscited by 11
  12. 2024Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Methodcited by 10
  13. 2023Attacking Perceptual Similarity Metricscited by 9
  14. 2019Hacking VMAF with Video Color and Contrast Distortioncited by 8
  15. 2021Hacking VMAF and VMAF NEG: vulnerability to different preprocessing methodscited by 8
  16. 2023Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metricscited by 7
  17. 2019Class Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusioncited by 3
  18. 2020AdvJND: Generating Adversarial Examples with Just Noticeable Differencecited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025Cross-Modal Transferable Image-to-Video Attack on Video Quality Metricscited by 8
  2. 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
  3. 2025SEGA: A Transferable Signed Ensemble Gaussian Black-Box Attack against No-Reference Image Quality Assessment Modelscited by 1
  4. 2024Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessmentcited by 0