Partial Gated Feedback Recurrent Neural Network for Data Compression Type Classification

Hyewon Song, Beom Kwon, Hoon Yoo, Sanghoon Lee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Owing to the widespread use of digital devices such as mobile phones and tablet PCs that are capable of easily viewing contents, the number of digital crimes committed using these digital devices has increased. One of the most common digital crimes is to hide the header information of the compressed data, which makes the user's data unusable. It is difficult to restore original data without the header because header contains the compression type. In this paper, we propose a Partial Gated Feedback Recurrent Neural Network (PGF-RNN) for the identification of lossless compression algorithms. We modify the gated recurrent units to improve the correlation of layers by grouping the fully-connected layers to effectively determine the characteristics of the compressed data. We emphasize on the temporal features, which consider a wide range of data, and spatial features from fully-connected layers to extract the feature vectors of each compression type. To improve the performance of the proposed PGF-RNN, we apply post-processing that considers the frequency of bit sequences on some compression types with similar compressed data. The proposed method is evaluated on 31 well-known lossless compression algorithms of the Association for Computational Linguistics dataset. The average top 1 accuracy of the proposed method is 92.63%.

Original languageEnglish
Article number9163346
Pages (from-to)151426-151436
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Funding Information:
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:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


Dive into the research topics of 'Partial Gated Feedback Recurrent Neural Network for Data Compression Type Classification'. Together they form a unique fingerprint.

Cite this