Assessment of deepfake detection techniques : a study of performance and generalisation [document électronique] /
Abderezak Mechenet, Auteur ;
Sid-Ahmed Berrani, Directeur de thèse . -
[S.l.] : [s.n.], 2024 . - 1 fichier PDF (9 Mo) : ill.
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2024
Bibliogr. p. 124 - 131 . - Annexe p. 132 - 134
Langues : Anglais (
eng)
| Mots-clés : |
Deepfake detection
Generalisation
Media content |
| Index. décimale : |
PN00224 |
| Résumé : |
Deepfakes are the fruit of development in the fields of Artificial Intelligence and Deep Learning, their appearance created a new field: deepfake detection, a domain that specialises in the verification of the authenticity of media content. Convolutional Neural Networks based detectors aim at detecting artifacts left by the generation process to draw conclusions on the multimedia. Our study was centralised around the assessment of existing deepfake detectors, aiming to evaluate and improve our diagnosed problem which is the generalisation ability across multiple datasets, our final product was put to the test on external videos to conclude on the hypothesis established. |
Assessment of deepfake detection techniques (2024)
