Abstract
Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective. We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability. We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers’ as it master the recent trends of CMFD and outline some future research directions.
Highlights
Digital image forgery has already showed up in many disturbing forms and results in inestimable lose [1]
This survey focuses on the passive forensic techniques proposed for copy-move forgery detection (CMFD) because Copy-Move Forgery (CMF) is a very challenging and popular forgery type
In keypoint based CMFD techniques, clustering of the matched pairs based on their location has two drawbacks: (i) the inability to separate the cloned region when www.ijacsa.thesai.org cloned region is close to its source region and (ii) the difficulty to identify the cloned region as a single region, when it contains scattered keypoints [59]
Summary
Digital image forgery has already showed up in many disturbing forms and results in inestimable lose [1]. Passive forensic techniques don't require any prior information about the image to be verified [10], [11] This survey focuses on the passive forensic techniques proposed for copy-move forgery detection (CMFD) because CMF is a very challenging and popular forgery type. Depending on visual similarity aims to detect CMF and isn’t able to detect any other forgery type It can localize the forged region along with its source region based on assessing their similarity. Depending on tampering artifacts is considered a general detection methodology for various forgery types Applying such methodology for CMFD is only able to localize the forged region without its authentic source region.
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