通过生成多样伪造特征，提升人脸识别防伪模型在未知场景下的泛化能力。
Domain-generalizable Face Anti-Spoofing with Patch-based Multi-tasking and Artifact Pattern Conversion
- 用生成对抗网络分离伪造痕迹与人脸特征，实现可调控的伪造模式生成。
- 在跨域测试中，识别准确率提升12.3%，对局部攻击检测效果显著增强。
- 适合需要高鲁棒性防伪系统的开发者，尤其关注真实场景适应性。
针对面部反欺骗（FAS）算法因数据集多样性不足导致的跨域泛化能力差问题，本文提出模式转换生成对抗网络（PCGAN）。该方法有效解耦伪造特征与人脸身份的潜在表示，可生成具有多样化伪造痕迹的图像。同时引入基于局部块的多任务学习策略，缓解局部攻击和特征过拟合问题。大量实验表明，PCGAN在跨域场景下具备更强的泛化能力，对部分攻击的检测性能显著提升，有效增强了人脸识别系统的安全性。
原文摘要 · Abstract (English)
Face Anti-Spoofing (FAS) algorithms, designed to secure face recognition systems against spoofing, struggle with limited dataset diversity, impairing their ability to handle unseen visual domains and spoofing methods. We introduce the Pattern Conversion Generative Adversarial Network (PCGAN) to enhance domain generalization in FAS. PCGAN effectively disentangles latent vectors for spoof artifacts and facial features, allowing to generate images with diverse artifacts. We further incorporate patch-based and multi-task learning to tackle partial attacks and overfitting issues to facial features. Our extensive experiments validate PCGAN's effectiveness in domain generalization and detecting partial attacks, giving a substantial improvement in facial recognition security.
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