# Deep Tree Learning for Zero-shot Face Anti-Spoofing

arXiv: https://arxiv.org/abs/1904.02860 · HTML: https://mlsanity.com/paper/1904.02860/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 30 of 73)

- [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)
- [Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2108.02667/) (2021)
- [Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment](https://mlsanity.com/paper/2303.13662/) (2023)
- [Dual-Cross Central Difference Network for Face Anti-Spoofing](https://mlsanity.com/paper/2105.01290/) (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)
- [Cross Modal Focal Loss for RGBD Face Anti-Spoofing](https://mlsanity.com/paper/2103.00948/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales](https://mlsanity.com/paper/2107.05821/) (2021)
- [Multi-domain Learning for Updating Face Anti-spoofing Models](https://mlsanity.com/paper/2208.11148/) (2022)
- [CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing](https://mlsanity.com/paper/2403.14333/) (2024)
- [Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://mlsanity.com/paper/2203.12175/) (2022)
- [Tame a Wild Camera: In-the-Wild Monocular Camera Calibration](https://mlsanity.com/paper/2306.10988/) (2023)
- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [Meta-Teacher For Face Anti-Spoofing](https://mlsanity.com/paper/2111.06638/) (2021)
- [Unified Detection of Digital and Physical Face Attacks](https://mlsanity.com/paper/2104.02156/) (2021)
- [Learning Meta Pattern for Face Anti-Spoofing](https://mlsanity.com/paper/2110.06753/) (2021)
- [Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2207.09868/) (2022)
- [Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era](https://mlsanity.com/paper/2202.10673/) (2022)
- [Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://mlsanity.com/paper/2208.05401/) (2022)
- [Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing](https://mlsanity.com/paper/2112.14894/) (2021)
- [ChatGPT-Powered Hierarchical Comparisons for Image Classification](https://mlsanity.com/paper/2311.00206/) (2023)
- [Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2103.01546/) (2021)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [Test-Time Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2403.19334/) (2024)
- [Structure Destruction and Content Combination for Face Anti-Spoofing](https://mlsanity.com/paper/2107.10628/) (2021)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data](https://mlsanity.com/paper/2303.02660/) (2023)
- [Unified Physical-Digital Face Attack Detection](https://mlsanity.com/paper/2401.17699/) (2024)

## Abstract (arXiv)

Face anti-spoofing is designed to keep face recognition systems from recognizing fake faces as the genuine users. While advanced face anti-spoofing methods are developed, new types of spoof attacks are also being created and becoming a threat to all existing systems. We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA only study 1-2 types of spoof attacks, such as print/replay attacks, which limits the insight of this problem. In this work, we expand the ZSFA problem to a wide range of 13 types of spoof attacks, including print attack, replay attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed to tackle the ZSFA. The tree is learned to partition the spoof samples into semantic sub-groups in an unsupervised fashion. When a data sample arrives, being know or unknown attacks, DTN routes it to the most similar spoof cluster, and make the binary decision. In addition, to enable the study of ZSFA, we introduce the first face anti-spoofing database that contains diverse types of spoof attacks. Experiments show that our proposed method achieves the state of the art on multiple testing protocols of ZSFA.