An explainable deepfake detection framework on a novel unconstrained dataset

Abstract In this work, we created a new large-scale unconstrained high-quality Deepfake Image (DFIM-HQ) dataset containing 140K images. Compared to existing datasets, this dataset includes a variety of diverse scenarios, pose variations, high-quality degradations, and illumination variations, making...

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Bibliografiske detaljer

Principais autores:	Sherin Mathews, Shivangee Trivedi, Amanda House, Steve Povolny, Celeste Fralick
Format:	Artigo
Sprog:	Inglês
Udgivet:	Springer 2023-01-01
Serier:	Complex & Intelligent Systems
Fag:	Deepfakes Deepfake image detection techniques Digital media forensics Deep learning Explainability AI bias
Online adgang:	https://doi.org/10.1007/s40747-022-00956-7
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