Face image synthesis detection is considerably gaining attention because of
the potential negative impact on society that this type of synthetic data
brings. In this paper, we propose a data-agnostic solution to detect the face
image synthesis process. Specifically, our solution is based on an anomaly
detection framework that requires only real data to learn the inference
process. It is therefore data-agnostic in the sense that it requires no
synthetic face images. The solution uses the posterior probability with respect
to the reference data to determine if new samples are synthetic or not. Our
evaluation results using different synthesizers show that our solution is very
competitive against the state-of-the-art, which requires synthetic data for
training.

我们提出了一种数据无关的解决方案，基于异常检测框架，并使用后验概率与参考数据相关，以确定新样本是否为合成的，评估结果显示我们的解决方案非常具有竞争力。

使用贝叶斯卷积神经网络进行数据不可知人脸图像合成检测

Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs

Synthetically generated face images have shown to be indistinguishable from
real images by humans and as such can lead to a lack of trust in digital
content as they can, for instance, be used to spread misinformation. Therefore,
the need to develop algorithms for detecting entirely synthetic face images is
apparent. Of interest are images generated by state-of-the-art deep
learning-based models, as these exhibit a high level of visual realism. Recent
works have demonstrated that detecting such synthetic face images under
realistic circumstances remains difficult as new and improved generative models
are proposed with rapid speed and arbitrary image post-processing can be
applied. In this work, we propose a multi-channel architecture for detecting
entirely synthetic face images which analyses information both in the frequency
and visible spectra using Cross Modal Focal Loss. We compare the proposed
architecture with several related architectures trained using Binary Cross
Entropy and show in cross-model experiments that the proposed architecture
supervised using Cross Modal Focal Loss, in general, achieves most competitive
performance.

通过使用跨媒体聚焦损失函数分析频率和可见光谱中的信息，我们提出了一种用于检测完全合成面部图像的多通道架构，并与几种使用二进制交叉熵训练的相关架构进行比较，在跨模型实验中展示了受跨媒体聚焦损失函数监督的所提出的架构通常具有最具竞争力的性能。