mlsanityData to 7 Oct 2026

Adversarial Color Film: Effective Physical-World Attack to DNNs

arXiv 2209.02430 ↗First posted 2 Sep 2022cs.CVcited by 11

Not on the map. Why?

Abstract (arXiv)

It is well known that the performance of deep neural networks (DNNs) is susceptible to subtle interference. So far, camera-based physical adversarial attacks haven't gotten much attention, but it is the vacancy of physical attack. In this paper, we propose a simple and efficient camera-based physical attack called Adversarial Color Film (AdvCF), which manipulates the physical parameters of color film to perform attacks. Carefully designed experiments show the effectiveness of the proposed method in both digital and physical environments. In addition, experimental results show that the adversarial samples generated by AdvCF have excellent performance in attack transferability, which enables AdvCF effective black-box attacks. At the same time, we give the guidance of defense against AdvCF by means of adversarial training. Finally, we look into AdvCF's threat to future vision-based systems and propose some promising mentality for camera-based physical attacks.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 37 of its 51 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

37 of 51 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  7. 2013Intriguing properties of neural networkscited by 5,114
  8. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  9. 2014Going Deeper with Convolutionscited by 4,231
  10. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  11. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  12. 2016Adversarial examples in the physical worldcited by 1,762
25 more
  1. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  2. 2016Universal adversarial perturbationscited by 774
  3. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 591
  4. 2017One pixel attack for fooling deep neural networkscited by 579
  5. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  6. 2017Adversarial Patchcited by 514
  7. 2018Physical Adversarial Examples for Object Detectorscited by 163
  8. 2018ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detectorcited by 159
  9. 2017EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examplescited by 154
  10. 2021Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical Worldcited by 127
  11. 2020Adversarial Camouflage: Hiding Physical-World Attacks with Natural Stylescited by 126
  12. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
  13. 2019ColorFool: Semantic Adversarial Colorizationcited by 89
  14. 2018Semantic Adversarial Examplescited by 86
  15. 2021Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blinkcited by 75
  16. 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
  17. 2019Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distancecited by 72
  18. 2017Robust Physical-World Attacks on Deep Learning Modelscited by 70
  19. 2019Adversarial camera stickers: A physical camera-based attack on deep learning systemscited by 69
  20. 2017Adversarial Attacks Beyond the Image Spacecited by 67
  21. 2018Maximal Jacobian-based Saliency Map Attackcited by 46
  22. 2021Optical Adversarial Attackcited by 43
  23. 2020Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Studycited by 40
  24. 2018Invisible Mask: Practical Attacks on Face Recognition with Infraredcited by 33
  25. 2019Physical Adversarial Textures that Fool Visual Object Trackingcited by 30

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 4; 2025: 1.

11 papers.

  1. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 66
  2. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  3. 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
  4. 2023Adversarial Examples in the Physical World: A Surveycited by 10
  5. 2023AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systemscited by 8
  6. 2023A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarkingcited by 5
  7. 2024Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systemscited by 3
  8. 2024Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systemscited by 2
  9. 2024The Fluorescent Veil: A Stealthy and Effective Physical Adversarial Patch Against Traffic Sign Recognitioncited by 2
  10. 2024Sparse and Transferable Universal Singular Vectors Attackcited by 1
  11. 2025A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challengescited by 1