Open Access

REVIEW

A Thorough Investigation on Image Forgery Detection

Anjani Kumar Rai*, Subodh Srivastava
Department of Electronics & Communication Engineering, NIT, Patna, India
* Corresponding Author: Anjani Kumar Rai. Email:

Computer Modeling in Engineering & Sciences 2023, 134(3), 1489-1528. https://doi.org/10.32604/cmes.2022.020920

Received 19 December 2021; Accepted 11 May 2022; Issue published 20 September 2022

Abstract

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

Keywords

Forgery detection; digital forgery; image forgery localization; image segmentation; image forensics; multimedia security

Cite This Article

Rai, A. K., Srivastava, S. (2023). A Thorough Investigation on Image Forgery Detection. CMES-Computer Modeling in Engineering & Sciences, 134(3), 1489–1528.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 228

    View

  • 203

    Download

  • 0

    Like

Share Link

WeChat scan