DeepFakes detection across generations: Analysis of facial regions, fusion, and performance evaluation
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
10.1016/j.engappai.2022.104673Engineering Applications of Artificial Intelligence 110 (2022): 104673
Funded byThis work has been supported by projects: PRIMA (H2020-MSCAITN-2019-860315), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), BIBECA, Spain (MINECO/FEDER RTI2018-101248-B-I00)
Projectinfo: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-I00
SubjectsBenchmark; Databases; DeepFakes; Face manipulation; Fake detection; Fake news; Media forensics; Telecomunicaciones
Rights© The author(s)
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Media forensics has attracted a tremendous attention in the last years in part due to the increasing concerns around DeepFakes. Since the release of the initial DeepFakes databases of the 1st generation such as UADFV and FaceForensics++ up to the latest databases of the 2nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an in-depth analysis of both 1st and 2nd DeepFakes generations in terms of fake detection performance. Two different methods are considered in our experimental framework: (i) the traditional one followed in the literature based on selecting the entire face as input to the fake detection system, and (ii) a novel approach based on the selection of specific facial regions as input to the fake detection system. Fusion techniques are applied both to the facial regions and also to three different state-of-the-art fake detection systems (Xception, Capsule Network, and DSP-FWA) in order to further increase the robustness of the detectors considered. Finally, experiments regarding intra- and inter-database scenarios are performed. Among all the findings resulting from our experiments, we highlight: (i) the very good results achieved using facial regions and fusion techniques with fake detection results above 99% Area Under the Curve (AUC) for UADFV, FaceForensics++, and Celeb-DF v2 databases, and (ii) the necessity to put more efforts on the analysis of inter-database scenarios to improve the ability of the fake detectors against attacks unseen during learning
Google Scholar:Tolosana Moranchel, Rubén - Romero-Tapiador, Sergio - Vera Rodríguez, Rubén - Gonzalez Sosa - Fiérrez Aguilar, Julián
This item appears in the following Collection(s)
Showing items related by title, author, creator and subject.
Tolosana Moranchel, Rubén; Vera Rodríguez, Rubén; Gonzalez-Garcia, Carlos; Fiérrez Aguilar, Julián; Morales Moreno, Aythami; Ortega García, Javier; Carlos Ruiz-Garcia, Juan; Romero-Tapiador, Sergio; Rengifo, Santiago; Caruana, Miguel; Jiang, Jiajia; Lai, Songxuan; Jin, Lianwen; Zhu, Yecheng; Galbally, Javier; Diaz, Moises; Angel Ferrer, Miguel; Gomez-Barrero, Marta; Hodashinsky, Ilya; Sarin, Konstantin; Slezkin, Artem; Bardamova, Marina; Svetlakov, Mikhail; Saleem, Mohammad; Lia Szcs, Cintia; Kovari, Bence; Pulsmeyer, Falk; Wehbi, Mohamad; Zanca, Dario; Ahmad, Sumaiya; Mishra, Sarthak; Jabin, Suraiya