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

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https://doi.org/10.1007/978-3-663-12959-2lished by the research community, having received the most attention in the last few years. In addition, we highlight in this chapter publicly available databases and code for the generation of digital fake content.

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Unterrichtenlernen und Forschenlerneninition and underlying challenges of the problem. Then, we present an overview of recent progress in talking face generation. In addition, we introduce some widely used datasets and performance metrics. Finally, we discuss open questions, potential future directions, and ethical considerations in this task.

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Face Morphing Attack Detection Methodsformance. Different concepts of morphing attack detection are introduced and state-of-the-art detection methods are evaluated in a comprehensive cross-database experiments considering various realistic image post-processings.

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Handbook of Digital Face Manipulation and DetectionFrom DeepFakes to Mo

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Digital Face Manipulation in Biometric Systemsation, it is shown that different types of face manipulation, i.e. retouching, face morphing, and swapping, can significantly affect the biometric performance of face recognition systems and hence impair their security. Eventually, this chapter provides an outlook on issues and challenges that face

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Multimedia Forensics Before the Deep Learning Era 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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Morph Creation and Vulnerability of Face Recognition Systems to Morphingss, 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