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Edge–texture feature-based image forgery detection with cross-dataset evaluation

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Abstract

A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs discriminative robust local binary patterns for encoding tampering traces and a support vector machine for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.

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Notes

  1. The FRITH dataset can be downloaded from http://users.cs.cf.ac.uk/Paul.Rosin/#data

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Acknowledgement

This research is supported by Higher Education Commission (HEC) Pakistan under International Research Support Initiative Program (IRSIP), grant # 1-8/HEC/HRD/2017/6950, and under Pakistan Program for Collaborative Research (PPCR), grant # 20-8/HEC/R&D/PPCR/2017, for the visit at School of Computer Science and Informatics, Cardiff University, UK, and PDE-GIR project which has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 778035, for the visit at Bournemouth University, UK.

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Authors and Affiliations

  1. Department of Computer Science, University of Okara, Okara, Pakistan

    Khurshid Asghar

  2. Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Mubbashar Saddique & Zulfiqar Habib

  3. Department of Computer Science, King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  4. School of Computer Science and Informatics, Cardiff University, Cardiff, UK

    Khurshid Asghar, Xianfang Sun & Paul L. Rosin

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  1. Khurshid Asghar
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  2. Xianfang Sun
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  4. Mubbashar Saddique
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  6. Zulfiqar Habib
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Correspondence to Zulfiqar Habib.

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Asghar, K., Sun, X., Rosin, P.L. et al. Edge–texture feature-based image forgery detection with cross-dataset evaluation. Machine Vision and Applications 30, 1243–1262 (2019). https://doi.org/10.1007/s00138-019-01048-2

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