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Authors: Kais Rouis 1 ; Petra Gomez-Krämer 1 ; Mickaël Coustaty 1 ; Saddock Kébairi 2 and Vincent Poulain d’Andecy 2

Affiliations: 1 L3i Laboratory, La Rochelle University, La Rochelle, France ; 2 R&D Department, Yooz, Aimargues, France

Keyword(s): Document Forgery, Manipulation Detection, Deep Neural Model, Residual Network.

Abstract: Image authenticity analysis has become a very important task in the last years with one main objective that is tracing the counterfeit content induced by illegal manipulations and forgeries that can be easily practiced using available software tools. In this paper, we propose a reliable residual-based deep neural network that is able to detect document image manipulations and copy-paste forgeries. We consider the perceptual characteristics of documents including mainly textual regions with homogeneous backgrounds. To capture abstract features, we introduce a shallow architecture using residual blocks and take advantage of shortcut connections. A first layer is implemented to boost the model performance, which is initialized with high-pass filters to forward low-level error feature maps. Manipulation experiments are conducted on a publicly available document dataset. We compare our method with two interesting forensic approaches that incorporate deep neural models along with first lay er initialization techniques. We carry out further experiments to handle the forgery detection problem on private administrative document datasets. The experimental results demonstrate the superior performance of our model to detect image manipulations and copy-paste forgeries in a realistic document fraud scenario. (More)

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Paper citation in several formats:
Rouis, K.; Gomez-Krämer, P.; Coustaty, M.; Kébairi, S. and Poulain d’Andecy, V. (2024). Deep Discriminative Feature Learning for Document Image Manipulation Detection. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 567-574. DOI: 10.5220/0012410800003660

@conference{visapp24,
author={Kais Rouis. and Petra Gomez{-}Krämer. and Mickaël Coustaty. and Saddock Kébairi. and Vincent {Poulain d’Andecy}.},
title={Deep Discriminative Feature Learning for Document Image Manipulation Detection},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={567-574},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012410800003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Deep Discriminative Feature Learning for Document Image Manipulation Detection
SN - 978-989-758-679-8
IS - 2184-4321
AU - Rouis, K.
AU - Gomez-Krämer, P.
AU - Coustaty, M.
AU - Kébairi, S.
AU - Poulain d’Andecy, V.
PY - 2024
SP - 567
EP - 574
DO - 10.5220/0012410800003660
PB - SciTePress

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