Transcription of FaceForensics++: Learning to Detect Manipulated Facial …
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FaceForensics++: Learning to Detect Manipulated Facial …
faceforensics ++: Learning to Detect Manipulated Facial ImagesAndreas R ossler1 Davide Cozzolino2 Luisa Verdoliva2 Christian Riess3 Justus Thies1 Matthias Nie ner11 Technical University of Munich2 University Federico II of Naples3 University of Erlangen-NurembergFigure 1: faceforensics ++is a dataset of Facial forgeries that enables researchers to train deep- Learning -based approachesin a supervised fashion. The dataset contains manipulations created with four state-of-the-art methods, namely,Face2 Face,FaceSwap,DeepFakes, rapid progress in synthetic image generation andmanipulation has now come to a point where it raises signif-icant concerns for the implications towards society. At best,this leads to a loss of trust in digital content, but could po-tentially cause further harm by spreading false informationor fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detectthem, either automatically or by standardize the evaluation of detection methods, wepropose an automated benchmark for Facial manipulationdetection1.
FaceForensics++: Learning to Detect Manipulated Facial Images Andreas Rossler¨ 1 Davide Cozzolino2 Luisa Verdoliva2 Christian Riess3 Justus Thies1 Matthias Nießner1 1Technical University of Munich 2University Federico II of Naples 3University of Erlangen-Nuremberg Figure 1: FaceForensics++ is a dataset of facial forgeries that enables researchers to train deep …
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