Detection of image manipulation with convolutional neural network and local feature descriptors

Abstract

In recent times, numerous digital image manipulation detection approaches have been proposed to detect which processing operations were applied to manipulate digital images. Most of these approaches consider the situation in which an image is manipulated by only one manipulation operation. However, practical image manipulation often involves multiple manipulation operations. It is important to detect multiple image manipulation operations and the order in which they were applied to establish the origin and genuineness of a given image as well as the processing history it has gone through. In this article, we proposed a new method to determine multiple image processing operation and operation chains based on convolutional neural network (CNN) and local optimal oriented pattern (LOOP). The proposed method is based on CNN and LOOP in which CNN extracts and learns image manipulation traces from the LOOP maps of the input images that are classified using softmax, extra-tree, and extreme gradient boosting (XGBOOST) classifiers. Detailed experiments show that the proposed model can attain overall detection accuracies of 99.81% and 99.15% in identifying different image manipulations and manipulation operation chains, respectively