Work

Forensics Analysis of Residual Noise Texture in digital Images for Detection of Deepfake

Year: 2025

Type: conference-paper

Abstract: This paper proposes an original approach for the automatic detection of AI-generated images, using features derived from noise residuals artefacts. Contrary to most current research that leverages sophisticated deep learning models to further improve performance, this study highlights the distinct noise residual characteristics in deepfakes, facilitating the identification of AI-generative images. Our findings highlight some limitations of image models, which can be used for forensic analysis and for future AI-based text-to-image generative models. Broad numerical results on a large and diverse dataset show the interest of the identified features as well as the relevance of the present method.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp49660.2025.10887712

Authors: Arthur Méreur, Antoine Mallet, Rémi Cogranne, Minoru Kuribayashi

Institutions: Université de Technologie de Troyes, Tohoku University

Language: English

Topic: Digital Media Forensic Detection

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Protection Against Criminal use of Steganography