Digital Image Forgery Detection Approaches: A Review and Analysis

15 Pages Posted: 25 Nov 2020

See all articles by Sabeena M

Sabeena M

APJ Abdul Kalam Techno logical university

Dr. Lizy Abraham

LBS Institute of Technology for Women

Date Written: November 21, 2020

Abstract

Digital images have an inevitable role in almost all areas like clinical imaging, media broadcasting, crime analysis and scientific analysis etc. The phrase, “One image is equal to a thousand words”, is exactly fit as an image has more expressive capability than text messages. In all investigation, a photograph was always perceived as ‘proof of occurrence of an event’, due to a strong observation that, ‘seeing is believing ‘. Hence, images were considered as a ‘piece of truth’. Normally, an image is authentic if it is an originally recorded or captured from an actual scene or situation using any image capturing device. The captured image is expected to convey the original situation or scene at the source while capturing it in a real sense. In recent decades, due to the accessibility of modern photograph content altering software tools, it is very easy to transform the content of the image and therefore authenticity and integrity of the image is meager and tampering in digital images therefore does not require any specialist skills. This paper presents the modern methodological assessment and analysis of recent image forgery detection techniques. The various methods used in each stage of forgery detection techniques are also briefed. For an immediate reference, the tables of comparison are provided. The subject review paper is intended to help researchers to deliver useful understandings and modernized info about ongoing progress in forgery detection.

Keywords: Forgery Detection, Survey, Digital Forensics

Suggested Citation

M, Sabeena and Abraham, Dr. Lizy, Digital Image Forgery Detection Approaches: A Review and Analysis (November 21, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3734785 or http://dx.doi.org/10.2139/ssrn.3734785

Sabeena M (Contact Author)

APJ Abdul Kalam Techno logical university ( email )

Dr. Lizy Abraham

LBS Institute of Technology for Women ( email )

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