Study of various Passive Copy-Move Image Forgery Detection Methods
Digital images are becoming increasingly prevalent in all aspects of human existence due to the significant technological advancement in the digital world. Although image forgery is no longer visible due to the emergence of the latest Digital Image-Editing Software programmes like Pic Monkey, Adobe Lightroom, Corel PaintShop, Skylum Luminar, and others, image forgery is now much more challenging to detect. The necessity for picture authentication and forgery detection is so critical in the digital age. This paper first describes the active and passive image forgery detection techniques currently in use. It then conducts a comparative study of several existing passive frequency-based copy-move forgery detection approaches based on evaluation metrics such as precision, recall, and F-Measure to determine which methods are the most effective. This paper also discusses the various forgery detection datasets, including their characteristics, advantages, and disadvantages, and the different practical evaluation metrics that researchers have used to evaluate the performance of image forgery detection algorithms in the real-world setting.