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Synthetically generated face images have shown to be indistinguishable fromreal images by humans and as such can lead to a lack of trust in digitalcontent as they can, for instance, be used to spread misinformation. Therefore,the need to develop algorithms for detecting entirely synthetic face images isapparent. Of interest are images generated by state-of-the-art deeplearning-based models, as these exhibit a high level of visual realism. Recentworks have demonstrated that detecting such synthetic face images underrealistic circumstances remains difficult as new and improved generative modelsare proposed with rapid speed and arbitrary image post-processing can beapplied. In this work, we propose a multi-channel architecture for detectingentirely synthetic face images which analyses information both in the frequencyand visible spectra using Cross Modal Focal Loss. We compare the proposedarchitecture with several related architectures trained using Binary CrossEntropy and show in cross-model experiments that the proposed architecturesupervised using Cross Modal Focal Loss, in general, achieves most competitiveperformance.

Multi-Channel Cross Modal Detection of Synthetic Face Images