(ICCV 2019) FSGAN: Subject Agnostic Face Swapping and Reenactment: We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is\nsubject agnostic and can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number of technical contributions. We derive a novel recurrent neural network (RNN)–based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence. For video sequences, we introduce continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving target skin color and lighting conditions. This network uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior. paper\n![\"\"](\"https://i.blogs.es/b2996b/ejemplo_fsganjpg/450_1000.jpg\")
(ICCV 2019) Deepfake Video Detection through Optical Flow based CNN: Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video frames, we propose the adoption of optical flow fields to exploit possible inter-frame dissimilarities. Such a clue is then used as feature to be learned by CNN classifiers. Preliminary results obtained on FaceForensics++ dataset highlight very promising performances. paper
(Sept 2019) Celeb-DF: A New Dataset for DeepFake Forensics: AI-synthesized face swapping videos, commonly known as the DeepFakes, have become an emerging problem recently. Correspondingly, there is an increasing interest in developing algorithms that can detect them. However, existing dataset of DeepFake videos suffer from low visual quality and abundant artifacts that do not reflect the reality of DeepFake videos circulated on the Internet. In this work, we present a new DeepFake dataset, Celeb-DF, for the development and evaluation of DeepFake detection algorithms. The Celeb-DF dataset is generated using a refined synthesis algorithm that reduces the visual artifacts observed in existing datasets. Based on the Celeb-DF dataset, we also benchmark existing DeepFake detection algorithms. |(Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, Siwei Lyu)| paper
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