# Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

arXiv: https://arxiv.org/abs/1912.07124 · HTML: https://mlsanity.com/paper/1912.07124/

## Key facts

- Cited by 19 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 15 Dec 2019.

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

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment](https://mlsanity.com/paper/2303.13662/) (2023)
- [Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing](https://mlsanity.com/paper/2105.02453/) (2021)
- [Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://mlsanity.com/paper/2203.12175/) (2022)
- [Gradient Alignment for Cross-Domain Face Anti-Spoofing](https://mlsanity.com/paper/2402.18817/) (2024)
- [Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2109.07950/) (2021)
- [FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection](https://mlsanity.com/paper/2501.02892/) (2025)
- [Fairness in Face Presentation Attack Detection](https://mlsanity.com/paper/2209.09035/) (2022)
- [Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics](https://mlsanity.com/paper/2308.14551/) (2023)
- [Unsupervised Compound Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2105.08463/) (2021)
- [Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection](https://mlsanity.com/paper/2301.02145/) (2023)
- [Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss](https://mlsanity.com/paper/2211.15955/) (2022)
- [Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2503.22984/) (2025)
- [Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing](https://mlsanity.com/paper/2307.02858/) (2023)
- [Saliency-based Video Summarization for Face Anti-spoofing](https://mlsanity.com/paper/2308.12364/) (2023)
- [Deep Learning meets Liveness Detection: Recent Advancements and Challenges](https://mlsanity.com/paper/2112.14796/) (2021)
- [A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing](https://mlsanity.com/paper/2306.14313/) (2023)
- [G2V2former: Graph Guided Video Vision Transformer for Face Anti-Spoofing](https://mlsanity.com/paper/2408.07675/) (2024)
- [Semi-Supervised learning for Face Anti-Spoofing using Apex frame](https://mlsanity.com/paper/2309.04958/) (2023)

## Abstract (arXiv)

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for bio-metric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.