TY - JOUR
AU - Verdoliva, Luisa
AB - [In recent years there have been astonishing advances inArtificial Intelligence (AI) AI-based synthetic media generationSynthetic media generation. Thanks to deep learningDeep learning methods it is now possible to generate visual data with a high level of realism. This is especiallySynthetic faceArtificial Intelligence (AI) true for human faces. Advanced deep learningDeep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic facesSynthetic face from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic faceSynthetic 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 the literature for the detection of synthetic facesSynthetic face. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.]
TI - Handbook of Digital Face Manipulation and Detection: Detection of AI-Generated Synthetic Faces
DA - 2022-01-31
UR - https://www.deepdyve.com/lp/springer-journals/handbook-of-digital-face-manipulation-and-detection-detection-of-ai-gmT6zG5dJr
DP - DeepDyve
ER -