mlsanityData to 7 Oct 2026

On Brightness Agnostic Adversarial Examples Against Face Recognition Systems

arXiv 2109.14205 ↗First posted 29 Sep 2021cs.CV cs.AI cs.LGcited by 9

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Abstract (arXiv)

This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 29 Sep 2021.

Builds on

10 of 14 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  6. 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 484
  7. 2016A study of the effect of JPG compression on adversarial imagescited by 196
  8. 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 180
  9. 2021Random Transformation of Image Brightness for Adversarial Attackcited by 3
  10. 2016Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographscited by 1

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 2.

9 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. 2021SoK: Anti-Facial Recognition Technologycited by 16
  5. 2022Visual Adversarial Attacks and Defenses in the Physical World: A Surveycited by 11
  6. 2022Powerful Physical Adversarial Examples Against Practical Face Recognition Systemscited by 7
  7. 2024A Survey on Physical Adversarial Attacks against Face Recognition Systemscited by 4
  8. 2024Non-Uniform Illumination Attack for Fooling Convolutional Neural Networkscited by 2
  9. 2023Simultaneous Adversarial Attacks On Multiple Face Recognition System Componentscited by 0