Dictionary based compression type classification using a CNN architecture

Hyewon Song; Beom Kwon; Seongmin Lee; Sanghoon Lee

doi:10.1109/APSIPAASC47483.2019.9023258

Dictionary based compression type classification using a CNN architecture

Hyewon Song, Beom Kwon, Seongmin Lee, Sanghoon Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	245-248
Number of pages	4
ISBN (Electronic)	9781728132488
DOIs	https://doi.org/10.1109/APSIPAASC47483.2019.9023258
Publication status	Published - 2019 Nov
Event	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China Duration: 2019 Nov 18 → 2019 Nov 21

Publication series

Name	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Conference

Conference	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Country/Territory	China
City	Lanzhou
Period	19/11/18 → 19/11/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea.

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

Information Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/APSIPAASC47483.2019.9023258

Cite this

Song, H., Kwon, B., Lee, S., & Lee, S. (2019). Dictionary based compression type classification using a CNN architecture. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 (pp. 245-248). [9023258] (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC47483.2019.9023258

Song, Hyewon ; Kwon, Beom ; Lee, Seongmin et al. / Dictionary based compression type classification using a CNN architecture. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 245-248 (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019).

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title = "Dictionary based compression type classification using a CNN architecture",

abstract = "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.",

author = "Hyewon Song and Beom Kwon and Seongmin Lee and Sanghoon Lee",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

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language = "English",

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Song, H, Kwon, B, Lee, S & Lee, S 2019, Dictionary based compression type classification using a CNN architecture. in 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019., 9023258, 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 245-248, 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019, Lanzhou, China, 19/11/18. https://doi.org/10.1109/APSIPAASC47483.2019.9023258

Dictionary based compression type classification using a CNN architecture. / Song, Hyewon; Kwon, Beom; Lee, Seongmin et al.

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 245-248 9023258 (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea. Publisher Copyright: © 2019 IEEE.

PY - 2019/11

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N2 - 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.

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Song H, Kwon B, Lee S, Lee S. Dictionary based compression type classification using a CNN architecture. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 245-248. 9023258. (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019). doi: 10.1109/APSIPAASC47483.2019.9023258

Dictionary based compression type classification using a CNN architecture

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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