Machine Learning Based Image Forgery Detection Using Natural Scene Statistics

A copy-move image forgery is the most common type of image tampering. It can be done by copying a part of an image and paste on another part of the same image. Therefore, it can be one of the challenging tasks to find that forgery. This paper suggested a different approach to detect the copy move image forgery by the natural scene statistic features. These features are extracted from both original and forged images of MICC-F2000 dataset. Natural scene statistics are the statistical properties of any natural image captured by any camera, so an attempt of forging an image makes these properties un-natural. By this method, an original and forged images can be easily classified by state-of-the-art machine learning models trained on these features. The performance of this method is quantitatively assessed using the famous evaluation metrics i-e accuracy, TPR, FPR, TNR, Recall and F1-score. A comparison with other state-of-the-art techniques has shown that the proposed technique has shown better results in comparison with the other techniques.