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A survey on digital image forensic methods based on blind forgery detection

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Abstract

In the current digital era, images have become one of the key channels for communication and information. There are multiple platforms where digital images are used as an essential identity, like social media platforms, chat applications, electronic and print media, medical science, forensics and criminal investigation, the court of law, and many more. Alternation of digital images becomes easy because multiple image editing software applications are accessible freely on the internet. These modified images can create severe problems in the field where the correctness of the image is essential. In such situations, the authenticity of the digital images from the bare eye is almost impossible. To prove the validity of the digital images, we have only one option: Digital Image Forensics (DIF). This study reviewed various image forgery and image forgery detection methods based on blind forgery detection techniques mainly. We describe the essential components of these approaches, as well as the datasets used to train and verify them. Performance analysis of these methods on various metrics is also discussed here.

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No additional data or material has been used for this work other than the referenced papers.

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No code has been developed by the authors for this work.

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Acknowledgements

I am (Deependra Kumar Shukla) grateful to the UGC and the Government of India for granting me the UGC- (JRF/SRF) fellowship, which enables me to pursue my research endeavors.

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Authors and Affiliations

  1. Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, 484887, India

    Deependra Kumar Shukla

  2. Department of Computer Science & Applications, Dr. Harisingh Gour Vishwavidyalaya, Sagar, Madhya Pradesh, 470003, India

    Abhishek Bansal

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, 305817, India

    Pawan Singh

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Shukla, D.K., Bansal, A. & Singh, P. A survey on digital image forensic methods based on blind forgery detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-18090-y

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