Titlebook: Handbook of Digital Face Manipulation and Detection; From DeepFakes to Mo Christian Rathgeb,Ruben Tolosana,Christoph Busch Book‘‘‘‘‘‘‘‘ 202

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Detection of AI-Generated Synthetic Facesentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in

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Capsule-Forensics Networks for Deepfake Detectionpter, we argue that our forensic-oriented capsule network overcomes these limitations and is more suitable than conventional CNNs to detect deepfakes. The superiority of our “Capsule-Forensics”network is due to the use of a pretrained feature extractor, statistical pooling layers, and a dynamic rout

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DeepFakes Detection: the  DeeperForensics  Dataset and Challengell number, of low quality, or overly artificial. Meanwhile, the large distribution gap between training data and actual test videos also leads to weak generalization ability. In this chapter, we present our on-going effort of constructing DeeperForensics-1.0, a large-scale forgery detection dataset,

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Book‘‘‘‘‘‘‘‘ 2022eadership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area..

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https://doi.org/10.1007/978-3-322-98878-2 some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review th

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Mediennutzung in Gesundheitsfachberufen,ss, in an overview ranging from the traditional techniques based on geometry warping and texture blending to the most recent and innovative approaches based on deep neural networks. Moreover, the sensitivity of state-of-the-art face recognition algorithms to the face morphing attack will be assessed