Digital Image Forgery Detection with Focus on a Copy-Move Forgery Detection: A Survey

The importance of ensuring the authenticity and reliability of digital images has grown significantly, primarily due to the ease of modifying such images with the progress of digital image editing tools. Consequently, there is a growing emphasis on the development of techniques for detecting image manipulation. One particular area of focus in digital image authentication is copy-move forgery detection. This paper presents a survey and a comparative study on copy-move forgery detection techniques in digital images, databases, and evaluation metrics. The study aims to provide insights into the effectiveness of different methods in detecting copy-move forgeries. The paper discusses prominent detection techniques, including block-based, keypoint-based, transform domain, hybrid methods, deep learning, and GAN approaches. The findings highlight the strengths, weaknesses, and key similarities and differences among the approaches. This study contributes to the understanding of the state-of-the-art in copy-move forgery detection and provides guidance for future research in this field.