DNA Steganalysis Using Deep Recurrent Neural Networks
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The technique of hiding messages in digital data is called a steganography technique. With improved sequencing techniques, increasing attempts have been conducted to hide hidden messages in deoxyribonucleic acid (DNA) sequences which have been become a medium for steganography. Many detection schemes have developed for conventional digital data, but these schemes not applicable to DNA sequences because of DNA's complex internal structures. In this paper, we propose the first DNA steganalysis framework for detecting hidden messages and conduct an experiment based on the random oracle model. Among the suitable models for the framework, splice junction classification using deep recurrent neural networks (RNNs) is most appropriate for performing DNA steganalysis. In our DNA steganography approach, we extract the hidden layer composed of RNNs to model the internal structure of a DNA sequence. We provide security for steganography schemes based on mutual entropy and provide simulation results that illustrate how our model detects hidden messages, independent of regions of a targeted reference genome. We apply our method to human genome datasets and determine that hidden messages in DNA sequences with a minimum sample size of 100 are detectable, regardless of the presence of hidden regions.
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