Intelligent Classification of JPEG files by Support Vector Machines with Content-based Feature Extraction

Abstract

Nowadays, multimedia files play a basic role in supporting evidence analysis for making decisions about a crime through looking at files as a digital guide or evidence. Multimedia files such as JPG images are a common format because many documents and memorial images on laptops are valuable. In addition, many JPG images on Laptops are valuable and have fewer structure contents, making recovery possible when their file system is missing. However, intelligent systems for fully recovering corrupted JPG images into their original form is a challenging research issue. In this research, a support vector machine (SVM) as intelligent classifier algorithm is proposed to classify JPG or non-JEG image clusters as part of multimedia files. The SVM classifies the data clusters on three content-based feature extraction (entropy, byte frequency distribution, and rate of change approach to derive cluster features) methods to optimize the identification of JPG image content. The SVM classifier is applied using a radial basis and polynomial kernel functions in MATLAB software. The experimental results show that the accuracy of classification of the SVM classifier with the polynomial function is 96.21%, and the SVM classifier with the radial basis function is 57.58%.