Learning to Generate Steganographic Cover for Audio Steganography Using GAN

Abstract

Audio steganography aims to exploit the human auditory redundancy to embed the secret message into cover audio, without raising suspicion when hearing it. However, recent studies have shown that the existing audio steganography can be easily exposed with the deep learning based steganalyzers by extracting high-dimensional features of stego audio for classification. The existing audio steganography schemes still have room for improvements. In this work, we propose an audio steganography framework that could automatically learn to generate superior steganographic cover audio for message embedding. Specifically, the training framework of the proposed framework consists of three components, namely, generator, discriminator and trained deep learning based steganalyzer. Then the traditional message embedding algorithm LSBM, is employed to embed the secret message into the steganographic cover audio to obtain stego audio, which is delivered to the trained steganalyzer for misclassifying as cover audio. Once the adversarial training is completed among these three parties, one can obtain a well-trained generator, which could generate steganographic cover audio for subsequent message embedding. In the practice of our proposed method, the stego audio is produced by embedding the secret message into the steganographic cover audio using a traditional steganography method. Experimental results demonstrate that our proposed audio steganography can yield steganographic cover audio that preserves a quite high perception quality for message embedding. We have compared the detection accuracies with the existing audio steganography schemes as presented in our experiment, the proposed method exhibits lower detection accuracies against the state-of-the-art deep learning based steganalyzers, under various embedding rates. Codes are publicly available at https://github.com/Chenlang2018/Audio-Steganography-using-GAN.INDEX TERMS Audio steganography, deep learning based steganalysis, generative adversarial network (GAN).

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“…The image steganography is divided into three parts, namely (1) least significant bits coding, (2) masking and filtering, and (3) transformations. Audio steganography Audio steganography is a technique that is used to transmit secret data by modifying a digitalized audio signal in an imperceptible manner [ 129 ]. Following types of audio steganography are given: (1) least significant bits coding, (2) phase coding, (3) spread spectrum, and (4) echo hiding.…”

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