A key point based copy-move forgery detection using HOG features

Image forgery detection is always a challenging task especially when it comes to the blind image forgery detection techniques which do not contain any water marks or signatures. Copy move forgery is a popular blind image forgery detection technique which is widely used by forgers for performing easy and efficient forgery. A copy-move forgery followed by rotation and scaling of the forged part is even more challenging to detect. In this paper, we present a copy move forgery detection algorithm which detects and localizes the forgery with reasonably good accuracy and in less time compared to other state-of-the-art methods. This method uses Harris Corner points to detect regions of interest and identifies a feature vector around this corner points using the Histogram of Oriented Gradients (HOG) descriptor. A comparison of the feature vectors is performed in the entire feature space to find a suitable match by using Sum of Squared Differences (SSD) and Nearest Neighbour Distance Ratio (NDR). The outlier matches in the previous step is eliminated by using RANSAC. The results are evaluated based on various performance measures like True Positive Rate (TPR), False Positive Rate (FPR), Precision and Recall. Datasets proposed by Adrizzone et al. and CoMoFoD(small) are used for evaluation. The results achieved show the efficiency of our method against moderate rotation and scaling with a reasonably good True Positive Rate.