mlsanityData to 14 Oct 2026

Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics

arXiv 2404.09961 ↗First posted 15 Apr 2024cs.CV eess.IVcited by 4

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Abstract (arXiv)

Objective no-reference image- and video-quality metrics are crucial in many computer vision tasks. However, state-of-the-art no-reference metrics have become learning-based and are vulnerable to adversarial attacks. The vulnerability of quality metrics imposes restrictions on using such metrics in quality control systems and comparing objective algorithms. Also, using vulnerable metrics as a loss for deep learning model training can mislead training to worsen visual quality. Because of that, quality metrics testing for vulnerability is a task of current interest. This paper proposes a new method for testing quality metrics vulnerability in the physical space. To our knowledge, quality metrics were not previously tested for vulnerability to this attack; they were only tested in the pixel space. We applied a physical adversarial Ti-Patch (Tiled Patch) attack to quality metrics and did experiments both in pixel and physical space. We also performed experiments on the implementation of physical adversarial wallpaper. The proposed method can be used as additional quality metrics in vulnerability evaluation, complementing traditional subjective comparison and vulnerability tests in the pixel space. We made our code and adversarial videos available on GitHub: https://github.com/leonenkova/Ti-Patch.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 45 references that are in the map. First posted on arXiv 15 Apr 2024.

Builds on

29 of 45 references.

References · 29

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2013Intriguing properties of neural networkscited by 5,155
  5. 2016Adversarial examples in the physical worldcited by 1,769
  6. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
  7. 2017One pixel attack for fooling deep neural networkscited by 582
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  10. 2019From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Qualitycited by 223
  11. 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 163
  12. 2021Admix: Enhancing the Transferability of Adversarial Attackscited by 156
17 more
  1. 2018DPatch: An Adversarial Patch Attack on Object Detectorscited by 147
  2. 2018LaVAN: Localized and Visible Adversarial Noisecited by 113
  3. 2020SoK: Certified Robustness for Deep Neural Networkscited by 105
  4. 2021Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognitioncited by 97
  5. 2019On Physical Adversarial Patches for Object Detectioncited by 93
  6. 2019E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensemblescited by 45
  7. 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
  8. 2019On adversarial patches: real-world attack on ArcFace-100 face recognition systemcited by 35
  9. 2022Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loopcited by 30
  10. 2022Video compression dataset and benchmark of learning-based video-quality metricscited by 27
  11. 2022Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metricscited by 17
  12. 2023Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attackscited by 14
  13. 2023Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attackscited by 12
  14. 2024Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Methodcited by 10
  15. 2024Black-box Adversarial Attacks Against Image Quality Assessment Modelscited by 8
  16. 2023Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metricscited by 7
  17. 2023eXplainable Artificial Intelligence (XAI) in aging clock modelscited by 2

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2025Cross-Modal Transferable Image-to-Video Attack on Video Quality Metricscited by 9
  2. 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
  3. 2024Secure Video Quality Assessment Resisting Adversarial Attackscited by 1
  4. 2024Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessmentcited by 0