Face Image Forgery Detection by Weight Optimized Neural Network Model

Abstract

This framework introduces a new automatic image forgery detection approach that involves four main stages like (i) Illumination map computation, (ii) Face detection, (iii) Feature extraction, and (iv) Classification. Initially, the processing of input image is exploited by means of illumination map estimation, which acquires two computation processes called Gray world estimates and Inverse-Intensity chromaticity. Subsequent to this, the Viola-Jones algorithm is employed for the face detection process, which is the second phase, in order to detect the face image clearly. Once after the detection process, the obtained facial image is subjected to feature extraction. For this, Grey Level Co-occurrence Matrix (GLCM) is exploited that extract the facial features from the image. After this, the classification process is carried out for the extracted facial features by employing the Neural Network (NN) classifier. On the whole, this paper mainly concerned over the optimization concept, in which the weight of the NN is optimally selected by using the renowned optimization algorithm named Whale Optimization Algorithm (WOA). To the end, the performance of the implemented model is compared over the other classical models like k-nearest neighbor (kNN), NN and Support Vector Machine (SVM) regarding certain measures like Accuracy, Sensitivity, and Specificity.