Image forgery detection using deeplearning by recompressing the images

Ali, Syed Sadaf, Ganapathi, Iyyakutti Iyappan, Vu, Ngoc-Son, Ali, Syed Danish, Saxena, Neetesh ORCID: https://orcid.org/0000-0002-6437-0807 and Werghi 2022. Image forgery detection using deeplearning by recompressing the images. Electronics 11 (3) , 403. 10.3390/electronics11030403

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Abstract

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher:	MDPI
ISSN:	2079-9292
Date of First Compliant Deposit:	24 February 2022
Date of Acceptance:	24 January 2022
Last Modified:	27 Jul 2023 18:19
URI:	https://orca.cardiff.ac.uk/id/eprint/146851

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