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Large-scale evaluation of splicing localization algorithms for web images

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Abstract

With the proliferation of smartphones and social media, journalistic practices are increasingly dependent on information and images contributed by local bystanders through Internet-based applications and platforms. Verifying the images produced by these sources is integral to forming accurate news reports, given that there is very little or no control over the type of user-contributed content, and hence, images found on the Web are always likely to be the result of image tampering. In particular, image splicing, i.e. the process of taking an area from one image and placing it in another is a typical such tampering practice, often used with the goal of misinforming or manipulating Internet users. Currently, the localization of splicing traces in images found on the Web is a challenging task. In this work, we present the first, to our knowledge, exhaustive evaluation of today’s state-of-the-art algorithms for splicing localization, that is, algorithms attempting to detect which pixels in an image have been tampered with as the result of such a forgery. As our aim is the application of splicing localization on images found on the Web and social media environments, we evaluate a large number of algorithms aimed at this problem on datasets that match this use case, while also evaluating algorithm robustness in the face of image degradation due to JPEG recompressions. We then extend our evaluations to a large dataset we formed by collecting real-world forgeries that have circulated the Web during the past years. We review the performance of the implemented algorithms and attempt to draw broader conclusions with respect to the robustness of splicing localization algorithms for application in Web environments, their current weaknesses, and the future of the field. Finally, we openly share the framework and the corresponding algorithm implementations to allow for further evaluations and experimentation.

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Notes

  1. Two Web platforms currently providing ELA analysis are FotoForensics (http://fotoforensics.com/), and Forensically (http://29a.ch/photo-forensics/#error-level-analysis).

  2. http://commoncrawl.org/

  3. http://bambuser.com/

  4. https://github.com/MKLab-ITI/image-forensics/tree/master/matlab_toolbox

  5. https://iapp.dinfo.unifi.it/index.php?page=source-code_en

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Acknowledgments

This work was supported by the REVEAL project, partially funded by the European Commission (contract no. FP7-610928).

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  1. Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou - Thermi, 57001, Thessaloniki, Greece

    Markos Zampoglou, Symeon Papadopoulos & Yiannis Kompatsiaris

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  1. Markos Zampoglou
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Correspondence to Markos Zampoglou.

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Zampoglou, M., Papadopoulos, S. & Kompatsiaris, Y. Large-scale evaluation of splicing localization algorithms for web images. Multimed Tools Appl 76, 4801–4834 (2017). https://doi.org/10.1007/s11042-016-3795-2

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