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Machine Vision and Applications

, Volume 30, Issue 7–8, pp 1243–1262 | Cite as

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

  • Khurshid Asghar
  • Xianfang Sun
  • Paul L. Rosin
  • Mubbashar Saddique
  • Muhammad Hussain
  • Zulfiqar HabibEmail author
Original Paper
  • 107 Downloads

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.

Keywords

Image forensics Image forgery detection Copy–move Splicing Cross-dataset evaluation 

Notes

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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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of OkaraOkaraPakistan
  2. 2.Department of Computer ScienceCOMSATS University Islamabad, Lahore CampusLahorePakistan
  3. 3.Department of Computer ScienceKing Saud UniversityRiyadhSaudi Arabia
  4. 4.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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