mlsanityData to 7 Oct 2026

CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

arXiv 2102.12642 ↗First posted 25 Feb 2021cs.CVcited by 6

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Abstract (arXiv)

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 25 Feb 2021.

Builds on

8 of 15 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  4. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  5. 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
  6. 2020CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotationscited by 109
  7. 2018Single Image Reflection Separation with Perceptual Lossescited by 98
  8. 2020Learning Generalized Spoof Cues for Face Anti-spoofingcited by 14

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 2.

6 papers.

  1. 2021Biometrics: Trust, but Verifycited by 39
  2. 2022Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Reviewcited by 6
  3. 2021Review of Face Presentation Attack Detection Competitionscited by 3
  4. 2023Wild Face Anti-Spoofing Challenge 2023: Benchmark and Resultscited by 2
  5. 2022Post-Train Adaptive MobileNet for Fast Anti-Spoofingcited by 1
  6. 2023AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systemscited by 0