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Enhancing copy-move forgery detection through a novel CNN architecture and comprehensive dataset analysis

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Abstract

In the contemporary digital era, images are omnipresent, serving as pivotal entities in conveying information, authenticating experiences, and substantiating facts. The ubiquity of image editing tools has precipitated a surge in image forgeries, notably through copy-move attacks where a portion of an image is copied and pasted within the same image to concoct deceptive narratives. This phenomenon is particularly perturbing considering the pivotal role images play in legal, journalistic, and scientific domains, necessitating robust forgery detection mechanisms to uphold image integrity and veracity. While advancements in Convolutional Neural Networks (CNN) have propelled copy-move forgery detection, existing methodologies grapple with limitations concerning the detection efficacy amidst complex manipulations and varied dataset characteristics. Additionally, a palpable void exists in comprehensively understanding and exploiting dataset heterogeneity to enhance detection capabilities. This heralds a pronounced exigency for innovative CNN architectures and nuanced understandings of dataset intricacies to augment detection capabilities, which has remained notably underexplored in the prevailing literature. Against this backdrop, our research broaches novel frontiers in copy-move forgery detection by introducing an innovative CNN architecture meticulously tailored to discern the subtlest manipulations, even amidst intricate image contexts. An extensive analysis of multiple datasets – MICC-F220, MICC-F600, and a combined variant – enables us to delineate a granular understanding of their attributes, thereby shedding unprecedented light on their influences on detection performance. Further, our research goes beyond mere detection, delving deep into comprehensive analyses of varied datasets and conducting additional experiments with differential training-validation sets and randomly labeled data to scrutinize the robustness and reliability of our model. We not only meticulously document and analyze our findings but also juxtapose them against extant models, offering an exhaustive comparative analysis.

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Data Availability

The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to express our deepest gratitude to Alexandra Pauls, our programmer, who played a pivotal role in conducting the experiments and uploading the source code of the software implementation to GitHub. Her dedication and expertise were invaluable to the success of this work.

Funding

1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101007820-TRUST.

2. This publication reflects only the author’s view and the REA is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 30/32, Macerata, 62100, Italy

    Oleksandr Kuznetsov & Emanuele Frontoni

  2. Department of Information and Communication Systems Security, V. N. Karazin Kharkiv National University, 4 Svobody Sq., Kharkiv, 61022, Ukraine

    Oleksandr Kuznetsov

  3. Department of Information Management and Business Systems, Comenius University Bratislava, Odbojárov 10, Bratislava, 820-05, Slovakia

    Oleksandr Kuznetsov

  4. Department of Information Engineering, Marche Polytechnic University, Via Brecce Bianche 12, Ancona, 60131, Italy

    Emanuele Frontoni, Luca Romeo & Riccardo Rosati

  5. Department Economics and Law, University of Macerata, Via Crescimbeni, 30/32, Macerata, 62100, Italy

    Luca Romeo

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  1. Oleksandr Kuznetsov
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  2. Emanuele Frontoni
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  3. Luca Romeo
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Contributions

• Conceptualization and methodology, writing-review and editing, Alexandr Kuznetsov;

• Formal analysis, investigation, Emanuele Frontoni;

• Resources, Luca Romeo;

• Data curation, Riccardo Rosati;

• All authors have read and agreed to the published version of the manuscript.

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Correspondence to Oleksandr Kuznetsov.

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Kuznetsov, O., Frontoni, E., Romeo, L. et al. Enhancing copy-move forgery detection through a novel CNN architecture and comprehensive dataset analysis. Multimed Tools Appl 83, 59783–59817 (2024). https://doi.org/10.1007/s11042-023-17964-5

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