Taylor‐RNet: An approach for image forgery detection using Taylor‐adaptive rag‐bull rider‐based deep convolutional neural network

Abstract

Due to the use of powerful computers and advanced software for photo editing, image manipulation in digital images simply degrades the trust in digital images. Image forensic analysis focuses on image authenticity and image content. To process forensic research, different methods are introduced, which effectively differentiate fake images from the original image. A technique named image splicing is commonly used for image tampering, and the tampered image may be used in photography contents, news reports, and so forth, which brings negative influences among the society. Thus, for detecting spliced images, this paper proposed an automatic forgery detection approach named Taylor-adaptive rag-bull rider (RR) optimization algorithm-based deep convolutional neural network (Taylor-RNet). At first, the face of a human is detected from the spliced image using the Viola Jones algorithm, and later, to estimate light coefficients, the three-dimensional (3D) shape of the face is determined by using a landmark-based 3D morphable model (L3DMM). Then, distance measures, like, Bhattacharya, Euclidean, Seuclidean, Chebyshev, correlation coefficients, and Hamming, are determined from the light coefficients that form the feature vector to the proposed Taylor-RNet, which identifies the spliced image. Taylor-adaptive RR is the integration of the Taylor series with the adaptive RR optimization algorithm. Finally, the experimental analysis is performed using four data sets, such as DSO-1, DSI-1, real data set, and hybrid data set. The analysis result of the proposed method obtained a maximum accuracy of 96.921%, true positive rate of 99.981%, and true negative rate of 99.783%.