However, the main problem the party faces is a public perception that its. An ablation study is performed to investigate the effect of the different components and style transfer losses. It was the last of the parties to be formed as a result of MMD succession. Table 8.3 demonstrates clustering accuracy, using MMD and MGD with normal and. Evaluations on various deep fake benchmarks (DFTIMIT, UADFV, Celeb-DF, and FaceForensics++) show that the proposed method achieves the best overall performance. MMI 47 database includes 19 different faces of students and research. This addition ensures that the learned features are shared by multiple domains and provides better generalization abilities to unseen deep fake samples. The center and triplet losses are added to enhance generalization. To this end, the maximum mean discrepancy (MMD) loss is incorporated to align the different feature distributions. Furthermore, artifacts such as imaging variations or face attributes do not persistently exist among all generated results for a single generation method.Therefore, in this paper, we propose a novel framework to address the domain gap induced by multiple deep fake datasets. A wallpaper or background (also known as a desktop wallpaper, desktop background, desktop picture or desktop image on computers) is a digital image (photo, drawing etc.) used as a decorative background of a graphical user interface on the screen of a computer, mobile communications device or other electronic device. The main drawback of existing face forensics detection methods is their limited generalization ability due to differences in domains. Face forensic detection is to distinguish manipulated from pristine face images.