Title: An intelligent approach to classify and detection of image forgery attack (scaling and cropping) using transfer learning

Authors: Ravi Sheth; Chandresh Parekha

Addresses: School of IT, AI and Cyber Security, Rashtriya Raksha University, Lavad, Dahegam, Gandhinagar, Gujarat, India ' School of IT, AI and Cyber Security, Rashtriya Raksha University, Lavad, Dahegam, Gandhinagar, Gujarat, India

Abstract: Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

Keywords: image forgery; scaling; cropping; deep learning; transform learning; ResNet.

DOI: 10.1504/IJICS.2024.141602

International Journal of Information and Computer Security, 2024 Vol.24 No.3/4, pp.322 - 337

Received: 30 May 2023
Accepted: 30 Oct 2023
Published online: 26 Sep 2024 *

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