Dictionary based Compression Type Classification using a CNN Architecture
Hyewon Song, Beom Kwon, Seongmin Lee, Sanghoon Lee · 2019
As digital devices which are capable of viewing contents easily such as mobile phones and tablet PCs have become widespread, the number of digital crimes using these digital contents also increases. Usually, the data which can be the evidence of crimes is compressed and the header of data is damaged to conceal the contents. Therefore, it is necessary to identify the characteristics of the entire bits of the compressed data to discriminate the compression type not using the header of the data. In this paper, we propose a method for distinguishing 16 dictionary-based compression types. We utilize 5-layered Convolutional Neural Network (CNN) for classification of compression type using Spatial Pyramid Pooling (SPP) layer. We evaluate our proposed method on the Wikileaks Dataset, which is a text file database. The average accuracy of 16 dictionary-based compression algorithms is 99%. We expect that our proposed method will be useful for providing evidence for Digital Forensics.