Deepfakes and beyond: A Survey of face manipulation and fake detection
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
ElsevierDate
2020-12-01Citation
10.1016/j.inffus.2020.06.014
Information Fusion 64 (2020): 131-148
ISSN
1566-2535 (print)DOI
10.1016/j.inffus.2020.06.014Funded by
This work has been supported by projects: PRIMA (H2020-MSCAITN-2019-860315), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), BIBECA (MINECO/FEDER RTI2018-101248-B-I00), Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017), and Accenture. Ruben Tolosana is supported by Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid y Fondo Social EuropeoProject
info:eu-repo/grantAgreement/EC/H2020/860315/EU/PriMa-ITN; info:eu-repo/grantAgreement/EC/H2020/860813/EU/TReSPAsS-ETN; Gobierno de España. RTI2018-101248-B-I00Editor's Version
https://doi.org/10.1016/j.inffus.2020.06.014Subjects
Benchmark; Databases; Deepfakes; Face manipulation; Face recognition; Fake news; Media forensics; TelecomunicacionesRights
© ElsevierAbstract
The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field
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Google Scholar:Tolosana Moranchel, Rubén
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Vera Rodríguez, Rubén
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Fiérrez Aguilar, Julián
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Morales Moreno, Aythami
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Ortega García, Javier
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