Robust cross-dataset deepfake detection with multitask self-supervised learning

Batagelj, Borut (avtor), Kronovšek, Andrej (avtor), Štruc, Vitomir (avtor), Peer, Peter (avtor)

Deepfake detection is increasingly critical due to the rise of manipulated media. Existing methods often require extensive datasets and struggle with interpretability issues. To address these issues, this study introduces a novel one-class approach for detecting and localizing deepfake artifacts in videos, using authentic images to generate manipulated data for training. By integrating segmentation and leveraging convolutional neural networks with visual transformers, the method predicts both the presence and location of the generated manipulations. Experiments on seven deepfake datasets and emerging diffusion-based manipulations show that our approach consistently outperforms existing methods, demonstrating superior accuracy and localization capabilities.

• Jezik: Angleški jezik
• Ključne besede: deepfake detection, one-class learning, segmentation, localization
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko; FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: Str. 1-6
• Številčenje: Vol. , no.
• PID: 20.500.12556/RUL-171520
• UDK: 004.93
• ISSN pri članku: 2405-9595
• DOI: 10.1016/j.icte.2025.02.011
• COBISS.SI-ID: 227912963
• Datum objave v RUL: 28.08.2025

Gradivo je del revije

• Naslov: ICT express
• Založnik: Elsevier
• ISSN: 2405-9595
• COBISS.SI-ID: 526132505

Licence

• Licenca: CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
• Opis: Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: zaznavanje globokih ponaredkov, enorazredno učenje, segmentacija, lokalizacija