Digital Image Forensic based on Machine Learning approach for Forgery Detection and Localization

Abstract

Machine learning for multimedia forensic is a new way of image forgery detection due to its amazing features of fast forgery detection. Compared with existing techniques of Deep Learning and Convolution Neural Network (“CNN”), machine learning improves security in the specific forged region under various test conditions. Some researchers use Support Vector Machine (“SVM”) and k-nearest neighbors (k-NN) algorithms to detect forgeries and another category uses unsupervised classification, including self-organization feature map (SOFM) and fuzzy c-means. But there occurs a need to address the detection speed improvement under the present scenario. The proposed algorithm has been developed using a machine learning approach to improve detection speed by pre-processing of feature extraction and feature reduction using “DWT” and “PCA” where data is trained by support vector machine (“SVM”) to provide quick results under various test conditions. This work specifies different image attacks like all types of geometric transformation, post-processing operations, etc., and presents efficiency in forgery detection and localization in case of multiple forgeries.