Multi-Channel Cross Modal Detection of Synthetic Face Images

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

Free Thermal Convection Driven by Nonlocal Effects

We report and explain a convective phenomenon observed in molecular dynamics simulations that cannot be classified either as a hydrodynamics instability nor as a macroscopically forced convection. Two complementary arguments show that the velocity field by a thermalizing wall is proportional to the ratio between the heat flux and the pressure. This prediction is quantitatively corroborated by our simulations.Comment: RevTex, figures is eps, submited for publicatio