Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

• Published in: Multimedia tools and applications Vol. 82; no. 12; pp. 18117 - 18150
• Main Authors: Sharma, Preeti, Kumar, Manoj, Sharma, Hitesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2023; Springer Nature B.V
• Subjects: Comparative studies; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Digital imaging; Forgery; Medical imaging; Multimedia Information Systems; Software; Special Purpose and Application-Based Systems; Image splicing; Deep learning-based detection techniques; Digital image forensics; GAN; Copy-move forgery detection; Data-driven methods
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-13808-w

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.