Article
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Preserved in Portico This version is not peer-reviewed
PP-JPEG: A Privacy-Preserving JPEG Image Tampering Localization
Version 1
: Received: 15 July 2023 / Approved: 17 July 2023 / Online: 17 July 2023 (08:07:17 CEST)
A peer-reviewed article of this Preprint also exists.
Jena, R.; Singh, P.; Mohanty, M. PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization. J. Imaging 2023, 9, 172. Jena, R.; Singh, P.; Mohanty, M. PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization. J. Imaging 2023, 9, 172.
Abstract
The widespread availability of digital image processing softwares has given rise to various forms of image manipulation and forgery which can pose a significant challenge in different fields such as law enforcement, journalism etc,. it can also lead a privacy concern. We are proposing a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for purpose of Investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images if the forged portion (dummy image) has a compression quality different from the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10x10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.
Keywords
Image Forensics; Tampering Localization; Copy Move Forgery; Paillier Encryption
Subject
Computer Science and Mathematics, Security Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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