Evaluation of copy-move forgery detection: datasets and evaluation metrics

Creating copy-move forgery became even easier using a wide range of software and platforms. Many algorithms have been proposed to solve the problem, but each one of those algorithms has its own drawbacks. Researchers face many challenges in developing copy-move detection algorithms, and in this paper, we focus on two challenges. The first is the benchmark dataset, and the second involves evaluation metrics. In this paper, we investigate the available copy-move datasets and their advantages and disadvantages. In addition, we discuss the different metrics that have been used by researchers to evaluate the copy-move forgery detection (CMFD) algorithms. On that basis, we suggest the standard specifications of the appropriate copy-move dataset and the metrics that should be used to evaluate the detection algorithms. The findings of this paper will help researchers evaluate their algorithms effectively and fairly essential for developing reliable algorithms.     

Enhanced block-based copy-move forgery detection using k-means clustering

Multidimensional Systems and Signal Processing - The goal of copy-move forgery is to manipulate the semantics of an image. In fact, this can be performed by cloning a region of an image and...