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.

本研究利用 Vision Transformer (ViT) 架构并结合 DINO 框架，对面部反欺诈任务进行了 Fine-tuning，与传统的 CNN 模型 EfficientNet b2 进行了性能比较。研究结果显示，ViT 模型在准确性和对不同欺诈方法的抵抗性方面优于 CNN 模型，进一步推动了生物识别安全领域的重要进展。

计算机视觉中的活体检测：基于 Transformer 的自监督学习用于人脸反欺骗

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

This paper presents the winning solution for the 1st SkatingVerse Challenge.
We propose a method that involves several steps. To begin, we leverage the DINO
framework to extract the Region of Interest (ROI) and perform precise cropping
of the raw video footage. Subsequently, we employ three distinct models, namely
Unmasked Teacher, UniformerV2, and InfoGCN, to capture different aspects of the
data. By ensembling the prediction results based on logits, our solution
attains an impressive leaderboard score of 95.73%.

本研究提供了在第一届 SkatingVerse Challenge 中的获胜解决方案，通过使用 DINO 框架提取兴趣区域（ROI）和精确裁剪原始视频素材，然后采用 Unmasked Teacher、UniformerV2 和 InfoGCN 三个不同的模型捕捉数据的不同方面，最终通过基于 logits 的预测结果集成获得了令人印象深刻的 95.73% 的排行榜分数。