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Image forgery detection: comprehensive review of digital forensics approaches

Author

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  • Satyendra Singh

    (University of Allahabad)

  • Rajesh Kumar

    (University of Allahabad)

Abstract

Image is a powerful way to share information in the digital world. The sources of images are everywhere, magazines, newspapers, healthcare, entertainment, education, social media and electronic media. With the advancement of image editing software and cheap camera-enabled mobile devices, image manipulation is very easy without any prior knowledge or expertise. So, image authenticity has questioned. Some people use the forged image for fun, but some people may have bad intentions. The manipulated image may use by political parties to spread their false propaganda. Fake images use by people to spread rumours and stoking someone. In addition to harming individuals, fake images can damage the credibility of media outlets and undermine the public trust in them. The need for reliable and efficient image forgery detection methods to combat misinformation, propaganda, hoaxes, and other malicious uses of manipulated images. These are some known issues on digital images. The researcher, scientist, and image forensic experts are working on the development of fake image detection and identification tools. Presently digital image forgery detection is a trending field of research. The main aim of this paper is to provide the exhaustive review on digital image forgery detection tools and techniques. It also discusses various machine learning techniques, such as supervised, unsupervised, and deep learning approaches, that can be employed for image forgery detection it demonstrate the challenges of the current state of the work.

Suggested Citation

  • Satyendra Singh & Rajesh Kumar, 2024. "Image forgery detection: comprehensive review of digital forensics approaches," Journal of Computational Social Science, Springer, vol. 7(1), pages 877-915, April.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00265-8
    DOI: 10.1007/s42001-024-00265-8
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    References listed on IDEAS

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    1. Nadheer Younus Hussien & Rasha O. Mahmoud & Hala Helmi Zayed, 2020. "Deep Learning on Digital Image Splicing Detection Using CFA Artifacts," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 12(2), pages 31-44, April.
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