# 3D High-Fidelity Mask Face Presentation Attack Detection Challenge

arXiv: https://arxiv.org/abs/2108.06968 · HTML: https://mlsanity.com/paper/2108.06968/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 55 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://mlsanity.com/paper/2011.02062/) (2020)
- [Local Relation Learning for Face Forgery Detection](https://mlsanity.com/paper/2105.02577/) (2021)
- [Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition](https://mlsanity.com/paper/2105.03162/) (2021)
- [Regularized Fine-grained Meta Face Anti-spoofing](https://mlsanity.com/paper/1911.10771/) (2019)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing](https://mlsanity.com/paper/2003.08061/) (2020)
- [CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing](https://mlsanity.com/paper/2003.05136/) (2020)
- [Face Anti-Spoofing Via Disentangled Representation Learning](https://mlsanity.com/paper/2008.08250/) (2020)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Face Anti-Spoofing with Human Material Perception](https://mlsanity.com/paper/2007.02157/) (2020)
- [A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing](https://mlsanity.com/paper/1812.00408/) (2018)
- [Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2108.02667/) (2021)
- [Dual-Cross Central Difference Network for Face Anti-Spoofing](https://mlsanity.com/paper/2105.01290/) (2021)
- [Dual Reweighting Domain Generalization for Face Presentation Attack Detection](https://mlsanity.com/paper/2106.16128/) (2021)
- [Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing](https://mlsanity.com/paper/2105.02453/) (2021)
- [Revisiting Pixel-Wise Supervision for Face Anti-Spoofing](https://mlsanity.com/paper/2011.12032/) (2020)
- [Learning Meta Model for Zero- and Few-shot Face Anti-spoofing](https://mlsanity.com/paper/1904.12490/) (2019)
- [Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.06148/) (2021)

## Built on by (most cited first, 19 of 19)

- [CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing](https://mlsanity.com/paper/2403.14333/) (2024)
- [Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition](https://mlsanity.com/paper/2303.15818/) (2023)
- [FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2305.03277/) (2023)
- [Flexible-Modal Face Anti-Spoofing: A Benchmark](https://mlsanity.com/paper/2202.08192/) (2022)
- [MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2304.07549/) (2023)
- [Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues](https://mlsanity.com/paper/2404.08450/) (2024)
- [La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detection](https://mlsanity.com/paper/2408.12793/) (2024)
- [Surveillance Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2304.07580/) (2023)
- [Review of Face Presentation Attack Detection Competitions](https://mlsanity.com/paper/2112.11290/) (2021)
- [AdvFAS: A robust face anti-spoofing framework against adversarial examples](https://mlsanity.com/paper/2308.02116/) (2023)
- [Unified Physical-Digital Attack Detection Challenge](https://mlsanity.com/paper/2404.06211/) (2024)
- [Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results](https://mlsanity.com/paper/2304.05753/) (2023)
- [Compact: Approximating Complex Activation Functions for Secure Computation](https://mlsanity.com/paper/2309.04664/) (2023)
- [FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model](https://mlsanity.com/paper/2404.09193/) (2024)
- [Unified Face Matching and Physical-Digital Spoofing Attack Detection](https://mlsanity.com/paper/2501.09635/) (2025)
- [Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection](https://mlsanity.com/paper/2512.06363/) (2025)
- [Robustness of Presentation Attack Detection in Remote Identity Validation Scenarios](https://mlsanity.com/paper/2602.00109/) (2026)
- [MS-MFAD : Multimodal large language models for Face Anti-spoofing Detection](https://mlsanity.com/paper/2608.17328/) (2026)
- [RoboFake: Are Deepfake Detectors Ready for Humanoid Face Robots?](https://mlsanity.com/paper/2610.14269/) (2026)

## Abstract (arXiv)

The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of a total amount of 54, 600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask-based attack detection. It attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranking algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.