Abstract
AbstractRecently, digital face manipulation and its detection have sparked large interest in industry and academia around the world. Numerous approaches have been proposed in the literature to create realistic face manipulations, such as DeepFakes and face morphs. To the human eye manipulated images and videos can be almost indistinguishable from real content. Although impressive progress has been reported in the automatic detection of such face manipulations, this research field is often considered to be a cat and mouse game. This chapter briefly discusses the state of the art of digital face manipulation and detection. Issues and challenges that need to be tackled by the research community are summarized, along with future trends in the field.
Highlights
Over the last couple of years, digital face manipulation and detection has become a highly active area of research
21 Future Trends in Digital Face Manipulation and Detection (DARPA), and competitions such as the Media Forensics Challenge (MFC2018)1 launched by the National Institute of Standards and Technology (NIST), the Deepfake Detection Challenge (DFDC)2 launched by Facebook, and the recent DeeperForensics Challenge
This concluding chapter has given an overview of different unsolved issues in the research field of digital face manipulation and detection
Summary
Ruben Tolosana, Christian Rathgeb, Ruben Vera-Rodriguez, Christoph Busch, Luisa Verdoliva, Siwei Lyu, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen, Peter Rot, Klemen Grm, Vitomir Štruc, Antitza Dantcheva, Zahid Akhtar, Sergio Romero-Tapiador, Julian Fierrez, Aythami Morales, Javier Ortega-Garcia, Els Kindt, Catherine Jasserand, Tarmo Kalvet, and Marek Tiits
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