An efficient method for image forgery detection based on trigonometric transforms and deep learning

Image forgery detection is the basic key to solve many problems, especially social problems such as those in Facebook, and court cases. The common form of image forgery is the copy-move forgery, in which a section of the image is copied and pasted in another location within the same image. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features of the copied parts are similar to those of the other parts of the image. This paper presents an approach for copy-move forgery detection based on block processing and feature extraction from the transforms of the blocks. In addition, a Convolutional Neural Network (CNN) is used for forgery detection. The feature extraction is implemented with serial pairs of convolution and pooling layers, and then classification between the original and tampered images is performed with and without transforms. A comparison study between different trigonometric transforms in 1D and 2D is presented for detecting the tampered parts in the image. This comparison study is based on the completeness rate for the detection. This comparison ensures that the DFT in 1D or 2D implementations is the best choice to detect copy-move forgery compared to other trigonometric transforms. In addition, the paper presents a comparison study between ten cases using the CNN learning technique to detect the manipulated image. The basic idea is to use a CNN to detect and extract features. The proposed CNN approach can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.