Image forgery detection using deep learning techniques

As a result of the advancement of software tools used in digital image processing, it has become very easy to generate fake images by applying various manipulation techniques on the original (authentic) images. These manipulated images can easily be used with malicious intentions in important fields such as law, medicine and communication. Hence, image forgery detection, determining whether an image is original or forged, is an important task. Many methods have been developed with different techniques in the field of image forgery detection. Today, deep learning methods are widely preferred over traditional methods for image forgery detection. These methods provide better performance than traditional image forgery detection methods because they extract complex features from the image. With the development of neural network technologies, convolutional neural networks are used in image forgery detection for the extraction of high-level image features recently. In this study, an image forgery detection system is proposed by combining three deep neural network structures in parallel, unlike the uniform deep learning methods used in image forgery detection. The proposed method has been evaluated on three different datasets, and the results clearly demonstrate the efficiency of the proposed method with promising classification accuracy.