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Automatic source scanner identification using 1D convolutional neural network

  • 1169: Interdisciplinary Forensics: Government, Academia and Industry Interaction
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Abstract

In this digital world, digitized documents can be considered original or a piece of evidence; checking the authenticity of any suspicious image has become an unavoidable concern to preserve the trust in its legitimacy. However, identifying the source of a digital image without any prior embedded information is a very challenging task. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN) model to solve the source scanner identification (SSI) problem blindly. Unlike traditional methods based on handcrafted features, the proposed framework can dynamically learn and extract scanner device-specific features. This work, comprised of the 1D-CNN and a support vector machine (SVM) as a classifier, was trained on nine scanners of different brands and models. The experimental result shows that our model achieves 98.15% accuracy on full images and overall accuracy of 93.13% on segments from test images, outperforming other state-of-art approaches. Our model also proves to be able to distinguish between scanners of the same model. Furthermore, the SVM classifier improved the 1D-CNN accuracy by approximately 3% compared to its original configuration.

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Acknowledgements

This work was financially supported by the “PHC Utique” program of the French Ministry of Foreign Affairs and Ministry of higher education and research and the Tunisian Ministry of higher education and scientific research in the CMCU project number 17G1405.

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Authors and Affiliations

  1. Technopôle Brest-Iroise, LaTIM Inserm UMR1101, IMT Atlantique, CS 83818, Plouzané, 29238 Brest Cedex 3, France

    Chaima Ben Rabah, Gouenou Coatrieux & Riadh Abdelfattah

  2. Higher School of Communications of Tunis, COSIM Lab, University of Carthage, El Ghazala City, Ariana, 2083, Tunisia

    Chaima Ben Rabah & Riadh Abdelfattah

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  1. Chaima Ben Rabah
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  2. Gouenou Coatrieux
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  3. Riadh Abdelfattah
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Correspondence to Chaima Ben Rabah.

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Ben Rabah, C., Coatrieux, G. & Abdelfattah, R. Automatic source scanner identification using 1D convolutional neural network. Multimed Tools Appl 81, 22789–22806 (2022). https://doi.org/10.1007/s11042-021-10973-2

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