VIOLA jones algorithm with capsule graph network for deepfake detection

Venkatachalam K; Pavel Trojovský; Štěpán Hubálovský

doi:10.7717/peerj-cs.1313

VIOLA jones algorithm with capsule graph network for deepfake detection

PeerJ Comput Sci. 2023 Apr 13:9:e1313. doi: 10.7717/peerj-cs.1313. eCollection 2023.

Authors

Venkatachalam K¹, Pavel Trojovský², Štěpán Hubálovský¹

Affiliations

¹ Department of Applied Cybernetics, Faculty of Science, University of Hradec Králová, Hradec Králová, Czech Republic.
² Department of Mathematics, Faculty of Science, University of Hradec Kralove, Hradec Kralove, Czech Republic.

Abstract

DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones's (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.

Keywords: Capsule graph network; Deep fake; Deep learning; Fake face detection; Machine learning; VIOLA Jones.

Grants and funding

The research was supported by the Excellence Project Faculty of Science, University of Hradec Králové, No. 2210/2023-2024. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.