DNA steganalysis using deep recurrent neural networks

Ho Bae, Byunghan Lee, Sunyoung Kwon, Sungroh Yoon

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are vari- ous methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods de- pend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution varia- tions by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools.

Original languageEnglish
Pages (from-to)88-99
Number of pages12
JournalPacific Symposium on Biocomputing
Issue number2019
StatePublished - 2019
Event24th Pacific Symposium on Biocomputing, PSB 2019 - Kohala Coast, United States
Duration: 3 Jan 20197 Jan 2019

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) [2014M3C9A3063541, 2018R1A2B3001628], and the Brain Korea 21 Plus Project in 2018.

Publisher Copyright:
© 2018 The Authors.


  • Deep recurrent neural network
  • DNA steganalysis
  • DNA steganography
  • DNA watermarking


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