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。

合成人脸图像的多通道跨模态检测

Multi-Channel Cross Modal Detection of Synthetic Face Images

The vulnerability of machine learning systems to adversarial attacks
questions their usage in many applications. In this paper, we propose a
randomized diversification as a defense strategy. We introduce a multi-channel
architecture in a gray-box scenario, which assumes that the architecture of the
classifier and the training data set are known to the attacker. The attacker
does not only have access to a secret key and to the internal states of the
system at the test time. The defender processes an input in multiple channels.
Each channel introduces its own randomization in a special transform domain
based on a secret key shared between the training and testing stages. Such a
transform based randomization with a shared key preserves the gradients in
key-defined sub-spaces for the defender but it prevents gradient back
propagation and the creation of various bypass systems for the attacker. An
additional benefit of multi-channel randomization is the aggregation that fuses
soft-outputs from all channels, thus increasing the reliability of the final
score. The sharing of a secret key creates an information advantage to the
defender. Experimental evaluation demonstrates an increased robustness of the
proposed method to a number of known state-of-the-art attacks.

本研究提出一种新的防御策略，即基于多通道架构和共享密钥的随机化方案，用以提高机器学习系统抵御对抗攻击的能力。实验结果表明，该方法能够有效增强系统对多种已知的对抗攻击的鲁棒性。