# SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

arXiv: https://arxiv.org/abs/2303.02660 · HTML: https://mlsanity.com/paper/2303.02660/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 51 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [Regularized Fine-grained Meta Face Anti-spoofing](https://mlsanity.com/paper/1911.10771/) (2019)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)
- [CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability](https://mlsanity.com/paper/2112.06592/) (2021)
- [SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data](https://mlsanity.com/paper/2206.10520/) (2022)
- [A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing](https://mlsanity.com/paper/1812.00408/) (2018)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [Revisiting Pixel-Wise Supervision for Face Anti-Spoofing](https://mlsanity.com/paper/2011.12032/) (2020)
- [Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.06148/) (2021)
- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [Unsupervised Face Recognition using Unlabeled Synthetic Data](https://mlsanity.com/paper/2211.07371/) (2022)
- [Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2103.01546/) (2021)
- [Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2109.07950/) (2021)
- [QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization](https://mlsanity.com/paper/2206.10526/) (2022)
- [Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection](https://mlsanity.com/paper/2208.05787/) (2022)

## Built on by (most cited first, 17 of 17)

- [IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models](https://mlsanity.com/paper/2308.04995/) (2023)
- [Synthetic Data for Face Recognition: Current State and Future Prospects](https://mlsanity.com/paper/2305.01021/) (2023)
- [Generative AI for Synthetic Data Generation: Methods, Challenges and the Future](https://mlsanity.com/paper/2403.04190/) (2024)
- [Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition](https://mlsanity.com/paper/2305.00358/) (2023)
- [ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation](https://mlsanity.com/paper/2307.05151/) (2023)
- [FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection](https://mlsanity.com/paper/2501.02892/) (2025)
- [Bias and Diversity in Synthetic-based Face Recognition](https://mlsanity.com/paper/2311.03970/) (2023)
- [Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics](https://mlsanity.com/paper/2308.14551/) (2023)
- [Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection](https://mlsanity.com/paper/2304.13419/) (2023)
- [Privacy-Safe Iris Presentation Attack Detection](https://mlsanity.com/paper/2408.02750/) (2024)
- [SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2311.05336/) (2023)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection](https://mlsanity.com/paper/2510.14314/) (2025)
- [Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark](https://mlsanity.com/paper/2607.26993/) (2026)
- [Sustainable Self-evolution Adversarial Training](https://mlsanity.com/paper/2412.02270/) (2024)
- [LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection](https://mlsanity.com/paper/2608.09633/) (2026)
- [Tomatoes, Potatoes, and Onions: Questioning the Need for Faces in Face Presentation Attack Detection](https://mlsanity.com/paper/2608.21455/) (2026)

## Abstract (arXiv)

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available.