# A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing

arXiv: https://arxiv.org/abs/1812.00408 · HTML: https://mlsanity.com/paper/1812.00408/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

## Built on by (most cited first, 30 of 51)

- [Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing](https://mlsanity.com/paper/2203.05340/) (2022)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [FLIP: Cross-domain Face Anti-spoofing with Language Guidance](https://mlsanity.com/paper/2309.16649/) (2023)
- [Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.06148/) (2021)
- [MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning](https://mlsanity.com/paper/2304.08028/) (2023)
- [Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://mlsanity.com/paper/2203.12175/) (2022)
- [Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition](https://mlsanity.com/paper/2303.15818/) (2023)
- [Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://mlsanity.com/paper/2208.05401/) (2022)
- [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)
- [3D High-Fidelity Mask Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2108.06968/) (2021)
- [Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing](https://mlsanity.com/paper/2302.05744/) (2023)
- [MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2304.07549/) (2023)
- [SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data](https://mlsanity.com/paper/2303.02660/) (2023)
- [Robust Multimodal Learning via Representation Decoupling](https://mlsanity.com/paper/2407.04458/) (2024)
- [Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation](https://mlsanity.com/paper/2309.09667/) (2023)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [Facial Depth and Normal Estimation using Single Dual-Pixel Camera](https://mlsanity.com/paper/2111.12928/) (2021)
- [Consistency Regularization for Deep Face Anti-Spoofing](https://mlsanity.com/paper/2111.12320/) (2021)
- [Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System](https://mlsanity.com/paper/2102.00713/) (2021)
- [Visual Prompt Flexible-Modal Face Anti-Spoofing](https://mlsanity.com/paper/2307.13958/) (2023)
- [Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues](https://mlsanity.com/paper/2404.08450/) (2024)
- [A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection](https://mlsanity.com/paper/2202.10286/) (2022)
- [VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and Adaptation](https://mlsanity.com/paper/2202.10301/) (2022)
- [H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness](https://mlsanity.com/paper/2204.13852/) (2022)
- [Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection](https://mlsanity.com/paper/2301.02145/) (2023)
- [Surveillance Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2304.07580/) (2023)
- [Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection](https://mlsanity.com/paper/2504.00458/) (2025)
- [Asymmetric Modality Translation For Face Presentation Attack Detection](https://mlsanity.com/paper/2110.09108/) (2021)
- [One-stage Modality Distillation for Incomplete Multimodal Learning](https://mlsanity.com/paper/2309.08204/) (2023)

## Abstract (arXiv)

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤negmedspace170) and modalities (≤negmedspace2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete