# Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing

arXiv: https://arxiv.org/abs/2406.13860 · HTML: https://mlsanity.com/paper/2406.13860/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 65 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [face anti-spoofing based on color texture analysis](https://mlsanity.com/paper/1511.06316/) (2015)
- [CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing](https://mlsanity.com/paper/1908.10654/) (2019)
- [Patch-level Representation Learning for Self-supervised Vision Transformers](https://mlsanity.com/paper/2206.07990/) (2022)
- [A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing](https://mlsanity.com/paper/1812.00408/) (2018)
- [On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/2011.08019/) (2020)
- [Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://mlsanity.com/paper/2203.12175/) (2022)
- [ViTAR: Vision Transformer with Any Resolution](https://mlsanity.com/paper/2403.18361/) (2024)
- [FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2305.03277/) (2023)
- [Adversarial Attacks on Foundational Vision Models](https://mlsanity.com/paper/2308.14597/) (2023)
- [From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection](https://mlsanity.com/paper/2304.03140/) (2023)
- [Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children](https://mlsanity.com/paper/2309.00138/) (2023)
- [Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO](https://mlsanity.com/paper/2206.06761/) (2022)
- [Robust face anti-spoofing framework with Convolutional Vision Transformer](https://mlsanity.com/paper/2307.12459/) (2023)
- [EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition](https://mlsanity.com/paper/2310.03754/) (2023)
- [Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing](https://mlsanity.com/paper/2401.04953/) (2024)

## Built on by (most cited first, 1 of 1)

- [Unified Face Matching and Physical-Digital Spoofing Attack Detection](https://mlsanity.com/paper/2501.09635/) (2025)

## Abstract (arXiv)

Face recognition systems are increasingly used in biometric security for convenience and effectiveness. However, they remain vulnerable to spoofing attacks, where attackers use photos, videos, or masks to impersonate legitimate users. This research addresses these vulnerabilities by exploring the Vision Transformer (ViT) architecture, fine-tuned with the DINO framework. The DINO framework facilitates self-supervised learning, enabling the model to learn distinguishing features from unlabeled data. We compared the performance of the proposed fine-tuned ViT model using the DINO framework against a traditional CNN model, EfficientNet b2, on the face anti-spoofing task. Numerous tests on standard datasets show that the ViT model performs better than the CNN model in terms of accuracy and resistance to different spoofing methods. Additionally, we collected our own dataset from a biometric application to validate our findings further. This study highlights the superior performance of transformer-based architecture in identifying complex spoofing cues, leading to significant advancements in biometric security.