Approaching Optimal Embedding in Audio Steganography with GAN

Document Type

Conference Proceeding

Publication Date

5-1-2020

Abstract

Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography. The embedding framework consists of three parts: a U-Net based generator, an embedding simulator, and a discriminator. For practical applications, Syndrome-Trellis Coding (STC) is used to generate stego audio with the learned embedding probability. Experimental results on the UME-ERJ and WSJ speech datasets have shown that the proposed framework can automatically learn the adaptive embedding probabilities for audio steganogra- phy and has a considerable advantage in terms of resisting steganalyzers in comparison with the existing conventional method.

Identifier

85089241721 (Scopus)

ISBN

[9781509066315]

Publication Title

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

External Full Text Location

https://doi.org/10.1109/ICASSP40776.2020.9054397

ISSN

15206149

First Page

2827

Last Page

2831

Volume

2020-May

Grant

61772571

Fund Ref

National Natural Science Foundation of China

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