Face anti-spoofing techniques based on domain generalization have recently been studied widely. Adversarial learning and meta-learning techniques have been adopted to learn domain-invariant representations. However, prior approaches often consider the dataset gap as the primary factor behind domain shifts. This perspective is not fine-grained enough to reflect the intrinsic gap among the data accurately. In our work, we redefine domains based on identities rather than datasets, aiming to disentangle liveness and identity attributes. We emphasize ignoring the adverse effect of identity shift, focusing on learning identity-invariant liveness representations through orthogonalizing liveness and identity features. To cope with style shifts, we propose Style Cross module to expand the stylistic diversity and Channel-wise Style Attention module to weaken the sensitivity to style shifts, aiming to learn robust liveness representations. Furthermore, acknowledging the asymmetry between live and spoof samples, we introduce a novel contrastive loss, Asymmetric Augmented Instance Contrast. Extensive experiments on four public datasets demonstrate that our method achieves state-of-the-art performance under cross-dataset and limited source dataset scenarios. Additionally, our method has good scalability when expanding diversity of identities. The codes will be released soon.
Deep Learning Models for Robust Facial Liveness Detection
Department of Theoretical and Applied Sciences, eCampus University（eCampus大学理论与应用科学系）；Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University（V. N. 卡拉津哈尔科夫国立大学计算机科学与人工智能学院智能软件系统与技术系）；Department of Political Sciences, Communication and International Relations, University of Macerata（马切拉塔大学政治学、传播与国际关系系）；Marche Polytechnic University（马尔凯理工大学）；Department Economics and Law, University of Macerata（马切拉塔大学经济与法律系）；Department of Information Engineering, Marche Polytechnic University（马尔凯理工大学信息工程系）
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
Nanyang Technological University（南洋理工大学）；Shenzhen University（深圳大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Confidence Aware Learning for Reliable Face Anti-spoofing
Key Laboratory of AI Safety of CAS, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS)（中国科学院计算技术研究所人工智能安全重点实验室）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）
Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Center for Research on Intelligent Perception and Computing（智能感知与计算研究中心）；Tianjin Academy for Intelligent Recognition Technologies（天津智能识别技术研究院）
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Baidu Research（百度研究院）；Great Bay University（大湾区大学）；Mininglamp Academy of Sciences, Mininglamp Technology（明略科技明略科学院）；Macau University of Science and Technology（澳门科技大学）；Westlake University（西湖大学）
Attention-Based Face AntiSpoofing of RGB Images, using a Minimal End-2-End Neural Network
Alpha Reality Co（阿尔法现实公司）；IRAN Broadcasting University (IRIBU)（伊朗广播大学）
人脸反欺骗是一种以识别人脸真伪为目标的技术，在活体检测、指纹识别等安全敏感应用中得到了广泛的关注，本文提出了两种基于卷积神经网络的端到端反欺骗系统。一个模型是基于EfficientNet B 0网络开发的，该网络在最终的密集层中进行了修改。第二个是MobileNet V2的一个非常轻的模型，它已经在基于Rose-Youtu数据集创建的数据上进行了有效的收缩，修改和重新训练。实验结果表明，这两种结构在检测人脸输入数据的真假图像上都取得了显著的效果。实验清楚地表明，重权重模型可以有效地用于服务器端实现，而低权重模型可以很容易地在手持设备上实现，并且仅使用RGB输入图像就可以很好地执行。
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions
College of Computer and Cyber Security, Fujian Normal University（计算机与网络安全学院，福建师范大学）；Department of Computer Science, National Tsing Hua University（计算机科学系，国立清华大学）
