Sharma Preeti, Kumar Manoj, Sharma Hitesh
School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007 India.
Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, United Arab Emirates.
Multimed Tools Appl. 2023;82(12):18117-18150. doi: 10.1007/s11042-022-13808-w. Epub 2022 Oct 1.
The digital image proves critical evidence in the fields like forensic investigation, criminal investigation, intelligence systems, medical imaging, insurance claims, and journalism to name a few. Images are an authentic source of information on the internet and social media. But, using easily available software or editing tools such as Photoshop, Corel Paint Shop, PhotoScape, PhotoPlus, GIMP, Pixelmator, etc. images can be altered or utilized maliciously for personal benefits. Various active, passive and other new deep learning technology like GAN approaches have made photo-realistic images difficult to distinguish from real images. Digital image tamper detection now focuses on determining the authenticity and consistency of digital photos. The major research problems use generic solutions and strategies, such as standardized data sets, benchmarks, evaluation criteria and generalized approaches.This paper overviews the evaluation of various image tamper detection methods. A brief discussion of image datasets and a comparative study of image criminological (forensic) methods are included in this paper. Furthermore, recently developed deep learning techniques along with their limitations have also been addressed. This study aims to comprehensively analyze image forgery detection methods using conventional and advanced deep learning approaches.
数字图像在法医调查、刑事侦查、情报系统、医学成像、保险理赔和新闻报道等领域都能提供关键证据。图像是互联网和社交媒体上可靠的信息来源。然而，使用诸如Photoshop、Corel Paint Shop、PhotoScape、PhotoPlus、GIMP、Pixelmator等容易获取的软件或编辑工具，图像可能会被恶意篡改或用于个人利益。各种主动、被动以及其他新的深度学习技术，如生成对抗网络（GAN）方法，使得逼真的图像难以与真实图像区分开来。数字图像篡改检测目前专注于确定数字照片的真实性和一致性。主要的研究问题采用通用的解决方案和策略，如标准化数据集、基准、评估标准和通用方法。本文概述了各种图像篡改检测方法的评估。本文还简要讨论了图像数据集，并对图像犯罪学（法医）方法进行了比较研究。此外，还介绍了最近开发的深度学习技术及其局限性。本研究旨在全面分析使用传统和先进深度学习方法的图像伪造检测方法。
