论文标题
关于视觉变压器对零射击的有效性
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
论文作者
论文摘要
面部识别系统对呈现攻击的脆弱性限制了其在安全至关重要的情况下的应用。自动检测这种恶意尝试的方法对于安全使用面部识别技术至关重要。尽管已经提出了各种检测此类攻击的方法，但其中大多数人过度介绍了训练集，并且无法推广到看不见的攻击和环境。在这项工作中，我们使用视觉变压器模型的转移学习来进行零射击反欺骗任务。通过公开可用的数据集实验证明了拟议方法的有效性。所提出的方法在HQ-WMCA和SIW-M数据集中的零摄像协议中优于最新方法。此外，该模型还可以显着提高跨数据库性能。
The vulnerability of face recognition systems to presentation attacks has limited their application in security-critical scenarios. Automatic methods of detecting such malicious attempts are essential for the safe use of facial recognition technology. Although various methods have been suggested for detecting such attacks, most of them over-fit the training set and fail in generalizing to unseen attacks and environments. In this work, we use transfer learning from the vision transformer model for the zero-shot anti-spoofing task. The effectiveness of the proposed approach is demonstrated through experiments in publicly available datasets. The proposed approach outperforms the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and SiW-M datasets by a large margin. Besides, the model achieves a significant boost in cross-database performance as well.
