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Image Splicing Forgery Detection Techniques: A Review

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1441))

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Abstract

The authenticity of digital images is openly challenged today due to the easy availability of various advanced image editing software. The semantic meaning of an image can be changed upto any extent with the help of these software. Image splicing forgery is one of the most popular ways to manipulate the content of an image. In image splicing forgery, two or more images or the parts of the images are used to create a spliced (composite) image. Spliced images can be misused in many ways. Therefore, to revive the trustworthiness of digital images, several efforts are made by researchers to develop various methods to detect image splicing forgery in the last few years. The main objective of this study is to review and analyze the recent work in this area. In this paper, first, a generalized workflow to detect image splicing forgery is presented. Second, this paper categorized the existing image splicing detection methods as hand-crafted feature-based and deep learning-based. Third, various publicly available image datasets are also summarized. Finally, future research directions are provided to help the researchers.

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Authors and Affiliations

  1. Jaypee University of Engineering and Technology, Raghogarh, Guna, MP, India

    Kunj Bihari Meena & Vipin Tyagi

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  1. Kunj Bihari Meena
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  2. Vipin Tyagi
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Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Meena, K.B., Tyagi, V. (2021). Image Splicing Forgery Detection Techniques: A Review. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_35

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  • DOI: https://doi.org/10.1007/978-3-030-88244-0_35

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