Learning generalized face anti-spoofing (FAS) models against presentation
attacks is essential for the security of face recognition systems. Previous FAS
methods usually encourage models to extract discriminative features, of which
the distances within the same class (bonafide or attack) are pushed close while
those between bonafide and attack are pulled away. However, these methods are
designed based on Euclidean distance, which lacks generalization ability for
unseen attack detection due to poor hierarchy embedding ability. According to
the evidence that different spoofing attacks are intrinsically hierarchical, we
propose to learn richer hierarchical and discriminative spoofing cues in
hyperbolic space. Specifically, for unimodal FAS learning, the feature
embeddings are projected into the Poincar\'e ball, and then the hyperbolic
binary logistic regression layer is cascaded for classification. To further
improve generalization, we conduct hyperbolic contrastive learning for the
bonafide only while relaxing the constraints on diverse spoofing attacks. To
alleviate the vanishing gradient problem in hyperbolic space, a new feature
clipping method is proposed to enhance the training stability of hyperbolic
models. Besides, we further design a multimodal FAS framework with Euclidean
multimodal feature decomposition and hyperbolic multimodal feature fusion &
classification. Extensive experiments on three benchmark datasets (i.e., WMCA,
PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed
method can bring significant improvement compared to the Euclidean baselines on
unseen attack detection. In addition, the proposed framework is also
generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP
REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types.

基于超几何空间和多模态特征的通用面部反欺骗方法在不同攻击类型下表现出显著的性能提升，并在少量攻击类型下具有很好的泛化能力。

双曲面人脸反欺骗

Hyperbolic Face Anti-Spoofing

This paper studies the problem of generalized zero-shot learning which
requires the model to train on image-label pairs from some seen classes and
test on the task of classifying new images from both seen and unseen classes.
Most previous models try to learn a fixed one-directional mapping between
visual and semantic space, while some recently proposed generative methods try
to generate image features for unseen classes so that the zero-shot learning
problem becomes a traditional fully-supervised classification problem. In this
paper, we propose a novel model that provides a unified framework for three
different approaches: visual-> semantic mapping, semantic->visual mapping, and
metric learning. Specifically, our proposed model consists of a feature
generator that can generate various visual features given class embeddings as
input, a regressor that maps each visual feature back to its corresponding
class embedding, and a discriminator that learns to evaluate the closeness of
an image feature and a class embedding. All three components are trained under
the combination of cyclic consistency loss and dual adversarial loss.
Experimental results show that our model not only preserves higher accuracy in
classifying images from seen classes, but also performs better than existing
state-of-the-art models in in classifying images from unseen classes.

本文研究了广义零样学习问题，提出了一种新的模型联合了三种不同的方法：视觉 -> 语义映射，语义 -> 视觉映射和度量学习，并且将它们统一在一个框架内，最终的实验结果表明该模型不仅在分类已知类别的图像上具有较高的准确性，而且在分类未知类别的图像上性能也比现有的最先进模型要更好。