Recently, significant progress has been made in face presentation attack
detection (PAD), which aims to secure face recognition systems against
presentation attacks, owing to the availability of several face PAD datasets.
However, all available datasets are based on privacy and legally-sensitive
authentic biometric data with a limited number of subjects. To target these
legal and technical challenges, this work presents the first synthetic-based
face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset.
The bona fide samples in SynthASpoof are synthetically generated and the attack
samples are collected by presenting such synthetic data to capture systems in a
real attack scenario. The experimental results demonstrate the feasibility of
using SynthASpoof for the development of face PAD. Moreover, we boost the
performance of such a solution by incorporating the domain generalization tool
MixStyle into the PAD solutions. Additionally, we showed the viability of using
synthetic data as a supplement to enrich the diversity of limited authentic
training data and consistently enhance PAD performances. The SynthASpoof
dataset, containing 25,000 bona fide and 78,800 attack samples, the
implementation, and the pre-trained weights are made publicly available.

本文介绍了一种基于合成数据的面部防伪数据集，名为 SynthASpoof，简要阐述了 MixStyle 的工作原理以及使用合成数据作为有限认证训练数据的补充，并给出了该数据集、实现和预训练权重的公开链接。

基于隐私友好合成数据的面部展示攻击检测 ——SynthASpoof

SynthASpoof: Developing Face Presentation Attack Detection Based on  Privacy-friendly Synthetic Data

Convolutional neural networks (CNNs) often have poor generalization
performance under domain shift. One way to improve domain generalization is to
collect diverse source data from multiple relevant domains so that a CNN model
is allowed to learn more domain-invariant, and hence generalizable
representations. In this work, we address domain generalization with MixStyle,
a plug-and-play, parameter-free module that is simply inserted to shallow CNN
layers and requires no modification to training objectives. Specifically,
MixStyle probabilistically mixes feature statistics between instances. This
idea is inspired by the observation that visual domains can often be
characterized by image styles which are in turn encapsulated within
instance-level feature statistics in shallow CNN layers. Therefore, inserting
MixStyle modules in effect synthesizes novel domains albeit in an implicit way.
MixStyle is not only simple and flexible, but also versatile -- it can be used
for problems whereby unlabeled images are available, such as semi-supervised
domain generalization and unsupervised domain adaptation, with a simple
extension to mix feature statistics between labeled and pseudo-labeled
instances. We demonstrate through extensive experiments that MixStyle can
significantly boost the out-of-distribution generalization performance across a
wide range of tasks including object recognition, instance retrieval, and
reinforcement learning.

该研究论文介绍了一种插入到 CNN 浅层中，名为 MixStyle 的插件，通过混合特征统计数值以提高 CNN 在领域转移方面的泛化性能和分布外泛化能力。MixStyle 不仅简单灵活而且用途广泛，可用于半监督领域泛化和无监督领域适应等问题。作者通过广泛的实验证明，MixStyle 可以显著提高各种任务的分布外泛化性能。

混合风格神经网络用于领域泛化与适应

MixStyle Neural Networks for Domain Generalization and Adaptation

Though convolutional neural networks (CNNs) have demonstrated remarkable
ability in learning discriminative features, they often generalize poorly to
unseen domains. Domain generalization aims to address this problem by learning
from a set of source domains a model that is generalizable to any unseen
domain. In this paper, a novel approach is proposed based on probabilistically
mixing instance-level feature statistics of training samples across source
domains. Our method, termed MixStyle, is motivated by the observation that
visual domain is closely related to image style (e.g., photo vs.~sketch
images). Such style information is captured by the bottom layers of a CNN where
our proposed style-mixing takes place. Mixing styles of training instances
results in novel domains being synthesized implicitly, which increase the
domain diversity of the source domains, and hence the generalizability of the
trained model. MixStyle fits into mini-batch training perfectly and is
extremely easy to implement. The effectiveness of MixStyle is demonstrated on a
wide range of tasks including category classification, instance retrieval and
reinforcement learning.

本研究介绍了一种新的基于概率混合实例级别特征统计的域泛化方法 MixStyle 来提高卷积神经网络模型的泛化性能，该方法着眼于图像风格与视觉域之间的关联关系，并将其应用于 CNN 的底层进行数据增广，既可用于分类任务还可应用于实例检索和强化学习等任务。