In recent years, Face Anti-Spoofing (FAS) has played a crucial role in
preserving the security of face recognition technology. With the rise of
counterfeit face generation techniques, the challenge posed by digitally edited
faces to face anti-spoofing is escalating. Existing FAS technologies primarily
focus on intercepting physically forged faces and lack a robust solution for
cross-domain FAS challenges. Moreover, determining an appropriate threshold to
achieve optimal deployment results remains an issue for intra-domain FAS. To
address these issues, we propose a visualization method that intuitively
reflects the training outcomes of models by visualizing the prediction results
on datasets. Additionally, we demonstrate that employing data augmentation
techniques, such as downsampling and Gaussian blur, can effectively enhance
performance on cross-domain tasks. Building upon our data visualization
approach, we also introduce a methodology for setting threshold values based on
the distribution of the training dataset. Ultimately, our methods secured us
second place in both the Unified Physical-Digital Face Attack Detection
competition and the Snapshot Spectral Imaging Face Anti-spoofing contest. The
training code is available at this https URL

通过可视化方法来直观反映模型的训练结果，并展示数据增强技术对跨领域 Face Anti-Spoofing 任务的有效性，同时基于训练数据集的分布提出了一种设置阈值的方法。最终在两个竞赛中获得了第二名。

CNN 网络数据领域变化的可视化方法及分类任务阈值选择的优化方法

A visualization method for data domain changes in CNN networks and the  optimization method for selecting thresholds in classification tasks

Support Vector Machine (SVM) is a powerful tool in binary classification,
known to attain excellent misclassification rates. On the other hand, many
realworld classification problems, such as those found in medical diagnosis,
churn or fraud prediction, involve misclassification costs which may be
different in the different classes. However, it may be hard for the user to
provide precise values for such misclassification costs, whereas it may be much
easier to identify acceptable misclassification rates values. In this paper we
propose a novel SVM model in which misclassification costs are considered by
incorporating performance constraints in the problem formulation. Specifically,
our aim is to seek the hyperplane with maximal margin yielding
misclassification rates below given threshold values. Such maximal margin
hyperplane is obtained by solving a quadratic convex problem with linear
constraints and integer variables. The reported numerical experience shows that
our model gives the user control on the misclassification rates in one class
(possibly at the expense of an increase in misclassification rates for the
other class) and is feasible in terms of running times.

我们提出了一种新颖的支持向量机模型，通过在问题表达中引入性能约束来考虑误分类成本。具体而言，我们的目标是寻求具有最大间隔的超平面，使得误分类率低于给定的阈值。通过解决一个具有线性约束和整数变量的二次凸问题来获得最大间隔超平面。我们的实验结果表明，我们的模型可以使用户在一个类别上对误分类率进行控制，并且运行时间可行。