# A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices

arXiv: https://arxiv.org/abs/2010.04145 · HTML: https://mlsanity.com/paper/2010.04145/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [Adversarial Sticker: A Stealthy Attack Method in the Physical World](https://mlsanity.com/paper/2104.06728/) (2021)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks](https://mlsanity.com/paper/2212.12995/) (2022)
- [ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection](https://mlsanity.com/paper/2203.01562/) (2022)
- [Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution](https://mlsanity.com/paper/2108.01077/) (2021)
- [Consistency Regularization for Deep Face Anti-Spoofing](https://mlsanity.com/paper/2111.12320/) (2021)
- [Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks](https://mlsanity.com/paper/2203.00481/) (2022)
- [Unsupervised Compound Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2105.08463/) (2021)
- [Shuffled Patch-Wise Supervision for Presentation Attack Detection](https://mlsanity.com/paper/2109.03484/) (2021)
- [Review of Face Presentation Attack Detection Competitions](https://mlsanity.com/paper/2112.11290/) (2021)
- [Impact of Face Image Quality Estimation on Presentation Attack Detection](https://mlsanity.com/paper/2209.15489/) (2022)
- [Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution](https://mlsanity.com/paper/2211.13964/) (2022)
- [Deep Learning meets Liveness Detection: Recent Advancements and Challenges](https://mlsanity.com/paper/2112.14796/) (2021)
- [IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images](https://mlsanity.com/paper/2309.17399/) (2023)
- [Spatiotemporal deep learning models for detection of rapid intensification in cyclones](https://mlsanity.com/paper/2506.08397/) (2025)
- [A Novel Face-Anti Spoofing Neural Network Model For Face Recognition And Detection](https://mlsanity.com/paper/2205.11240/) (2022)
- [RealFace -- Pedestrian Face Dataset](https://mlsanity.com/paper/2409.00283/) (2024)
- [A Human-Centered Risk Evaluation of Biometric Systems Using Conjoint Analysis](https://mlsanity.com/paper/2409.11224/) (2024)

## Abstract (arXiv)

The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue. This survey thoroughly investigates the face Presentation Attack Detection (PAD) methods, that only require RGB cameras of generic consumer devices, over the past two decades. We present an attack scenario-oriented typology of the existing face PAD methods and we provide a review of over 50 of the most recent face PAD methods and their related issues. We adopt a comprehensive presentation of the methods that have most influenced face PAD following the proposed typology, and in chronological order. By doing so, we depict the main challenges, evolutions and current trends in the field of face PAD, and provide insights on its future research. From an experimental point of view, this survey paper provides a summarized overview of the available public databases and extensive comparative experimental results of different PAD methods.