Classification of JPEG files by using extreme learning machine - Ali, Rabei Raad; Mohamad, Kamaruddin Malik; Jamel, Sapiee; Ahmad Khalid, Shamsul Kamal (SPRINGER, 2018)
Recovery of data files when their system information missing is a challenging research issue. The recovery process entails methods that analyze the structure and contents of each individual file clusters. A primary and important process of files’ recovery is determining the files’ types including JPEG, DOC or HTML. This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. The algorithm automatically classifies the files based on evaluation measures of three methods Entropy, Byte Frequency Distribution and Rate of Change. The ELM algorithm is applied to RABEI-2017 and DFRWS-2006 datasets. The experimental results show that the ELM algorithm is able to identify JPEG files of fragmented clusters with high accuracy rate. The classification accuracy of the RABEI-2017 dataset is 90.15 % and the DFRWS-2006 is 93.46%. The DFRWS-2006 has more classes than the RABEI-2017 which improves the ELM classifier fitting.