Video forgery detection using HOG features and compression properties

In this paper, we propose a novel video forgery detection technique to detect the spatial and temporal copy paste tampering. It is a challenge to detect the spatial and temporal copy-paste tampering in videos as the forged patch may drastically vary in terms of size, compression rate and compression type (I, B or P) or other changes such as scaling and filtering. In our proposed algorithm, the copy-paste forgery detection is based on Histogram of Oriented Gradients (HOG) feature matching and video compression properties. The benefit of using HOG features is that they are robust against various signal processing manipulations. The experimental results show that the forgery detection performance is very effective. We also compare our results against a popular copy-paste forgery detection algorithm. In addition, we analyze the experimental results for different forged patch sizes under varying degree of modifications such as compression, scaling and filtering.