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

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Volker Nitzschke,Jürgen Langhammer-Jaeschkevelop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, ., which contains 5, 639

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Regine Breusing,Solveig Steinmann-Lindnerts. The concern for the impact of the widespread deepfake videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes are for both human and computer visions by showing how well these videos can fool face recognition algorithms and naïve human

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Mediennutzung in Gesundheitsfachberufen,systems in controlled scenarios. However, even under these desirable conditions, digital image alterations can severely affect the recognition performance. In particular, several studies show that automatic face recognition systems are very sensitive to the so-called face morphing attack, where face

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https://doi.org/10.1007/978-3-662-53963-7ep neural networks has opened up the possibility of scaling it to multiple applications. Despite the improvement in performance, deep network-based Face Recognition Systems (FRS) are not well prepared against adversarial attacks at the deployment level. The output performance of such FRS can be dras

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Unterrichtenlernen und Forschenlernenm applications, such as teleconferencing, movie dubbing, and virtual assistant. The emergence of deep learning and cross-modality research has led to many interesting works that address talking face generation. Despite great research efforts in talking face generation, the problem remains challengin

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Horst Schecker,Dietmar Hötteckechnically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of s

BOOR

https://doi.org/10.1007/978-3-658-22513-1ble easy, credible manipulations of multimedia assets. Some even utilize advanced artificial intelligence concepts to manipulate media, resulting in videos known as .. Social media platforms and their “echo chamber” effect propagate fabricated digital content at scale, sometimes with dire consequenc

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,Lerneffektivität ausgewählter Methoden,rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the re

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