mlsanityData to 7 Oct 2026

Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics

arXiv 2305.15544 ↗First posted 24 May 2023cs.CV cs.MM eess.IVcited by 7

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Abstract (arXiv)

Modern neural-network-based no-reference image- and video-quality metrics exhibit performance as high as full-reference metrics. These metrics are widely used to improve visual quality in computer vision methods and compare video processing methods. However, these metrics are not stable to traditional adversarial attacks, which can cause incorrect results. Our goal is to investigate the boundaries of no-reference metrics applicability, and in this paper, we propose a fast adversarial perturbation attack on no-reference quality metrics. The proposed attack (FACPA) can be exploited as a preprocessing step in real-time video processing and compression algorithms. This research can yield insights to further aid in designing of stable neural-network-based no-reference quality metrics.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

12 of 19 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  7. 2016Adversarial examples in the physical worldcited by 1,762
  8. 2019Quality Assessment of In-the-Wild Videoscited by 164
  9. 2020Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Trainingcited by 72
  10. 2020Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessmentcited by 54
  11. 2022Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loopcited by 30
  12. 2022Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metricscited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 1; 2026: 1.

7 papers.

  1. 2024Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularizationcited by 7
  2. 2024Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metricscited by 5
  3. 2024IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metricscited by 4
  4. 2024Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metricscited by 4
  5. 2026BiRQA: Bidirectional Robust Quality Assessment for Imagescited by 1
  6. 2024Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessmentcited by 0
  7. 2025Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbationscited by 0