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A detailed analysis of image and video forgery detection techniques

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Abstract

With the recent advancement in modern technology, one can easily manipulate a digital image or video using computer software or a mobile application. The purpose of editing visual media could be as simple as to look good before sharing to the social networking site’s or can be as malicious as to defame or hurt one’s reputation in the real world through such morphed visual imagery. Identity theft is one of the examples where one’s identity get stolen by some impersonator who can access the personal and financial information of an innocent person. To avoid such drastic situations, law enforcement authorities must use some automatic tools and techniques to find out whether a person is innocent or the culprit. One major question that arises here is how and what parts of visual imagery can be manipulated or edited. The answer to this question is important to distinguish the authentic images/videos from the doctored multimedia. This survey provides a detailed analysis of image and video manipulation types, popular visual imagery manipulation methods, and state-of-the-art image and video forgery detection techniques. It also surveys different fake image and video datasets used in tampering. The goal is to develop a sense of privacy and security in the research community. Finally, it focuses to motivate researchers to develop generalized methods to capture artificial visual imagery which is capable of detecting any type of manipulation in given visual imagery.

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Notes

  1. https://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.html.

  2. http://elib.cs.berkeley.edu/photos/use.html.

  3. https://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/.

  4. https://github.com/namtpham/casia1groundtruth.

  5. https://ieee-dataport.org/open-access/modified-casia#files.

  6. http://lci.micc.unifi.it/labd/2015/01/copy-move-forgery-detection-and-localization/.

  7. https://www5.cs.fau.de/research/data/image-manipulation/.

  8. https://www.vcl.fer.hr/comofod/.

  9. https://signalprocessingsociety.org/newsletter/2013/06/ifs-tc-image-forensics-challenge.

  10. https://mklab.iti.gr/results/the-wild-web-tampered-image-dataset/.

  11. https://www.nist.gov/itl/iad/mig/open-media-forensics-challenge.

  12. https://mfc.nist.gov.

  13. https://github.com/wenbihan/coverage.

  14. http://loki.disi.unitn.it/RAISE/index.php.

  15. https://qualinet.github.io/databases/image/uncompressed_colour_image_database_ucid/.

  16. https://github.com/shaoanlu/faceswap-GAN.

  17. https://github.com/ondyari/FaceForensics.

  18. https://www.youtube.com/channel/UCKpH0CKltc73e4wh0_pgL3g.

  19. GitHub: https://github.com/EndlessSora/DeeperForensics-1.0.

  20. https://github.com/deepfakes/faceswap.

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  1. National Institute of Technology, Hamirpur, India

    Shobhit Tyagi & Divakar Yadav

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Tyagi, S., Yadav, D. A detailed analysis of image and video forgery detection techniques. Vis Comput 39, 813–833 (2023). https://doi.org/10.1007/s00371-021-02347-4

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