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%.

No abstract

No abstract

PurposeWith the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.Design/methodology/approachThis paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.FindingsExperimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.Originality/valueThe Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

Information in the form of the image conveys more details than any other form of information. Several software packages are available to manipulate the images so that the authenticity of the images is being questioned. Several image processing approaches are available to create fake images without leaving any visual clue about the forging operation. So, proper image forgery detection tools are required to detect such forgery images. Over the past few years, several research papers were published in the digital image forensics domain for detecting fake images, thus escalating the legitimacy of the images. This survey paper attempts to review the recent approaches proposed for detecting image forgery. Accordingly, several research papers related to image forgery detection are reviewed and analyzed. The taxonomy of image forgery detection techniques is presented, and the algorithms related to each technique are discussed. The comprehensive analysis is carried out based on the dataset used, software used for the implementation and the performance achievement. Besides, the research issues associated with every approach were scrutinized together with the recommendation for future work.