Abstract:
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 advanced techniques
has shown that the presented technique has shown more better results in comparison with the other
techniques.
