Siamese convolutional neural network-based approach towards universal image forensics

Abstract

This study proposes a novel deep learning-based method which can detect different types of image editing operations carried out on images. Unlike most of the existing methods, which can only detect the editing operations considered in the training stage, the proposed method can generalise to manipulations not seen in the training stage. The method is based on the classification of image pairs as either similarly or differently processed using a deep siamese neural network. Once the network learns features that can discriminate different editing operations, it can check whether an image is processed with an editing operation, not present in the training stage, using the one-shot classification strategy. An image forgery detection and localisation technique is also proposed using the trained siamese network. The experimental results show the efficacy of the proposed method in detecting different editing operations and also show the ability in detecting and localising image forgeries.