Passive Copy-Move Image Forgery Detection Techniques: A Study
Due to the tremendous technological development in the digital world, there is a proliferation in the popularity of digital images in all spheres of human life. However, the introduction of state of the art Digital Image-Editing Software packages such as Pic Monkey, Adobe Lightroom, Corel PaintShop, Skylum Luminar, etc. have made image forgery non-observable and much easier than earlier times. Thus, there is a need for image authentication and forgery detection. This paper presents the active and passive image forgery detection techniques in use and then draws a comparative study of several existing passive frequency-based copy-move forgery detection approaches depending on evaluation metrics such as precision, recall and F-Measure. This paper also discusses the various forgery detection datasets with their features, advantages, and disadvantages along with the different practical evaluation metrics used by researchers for assessing the performance of the image forgery detection algorithms.