Towards breaking DNN-based audio steganalysis with GAN
by Jie Wang; Rangding Wang; Li Dong; Diqun Yan; Xueyuan Zhang; Yuzhen Lin
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 14, No. 4, 2021

Abstract: Recently, deep neural network (DNN) has significantly boosted the performance of audio steganalysis. Accordingly, most of the traditional steganography cannot resist such DNN-based steganalysis. In this work, we attempt to break a given DNN-based audio steganalysis method with a prespecified steganography. To achieve this goal, we propose to employ a generative adversarial network (GAN) to generate an enhanced cover audio firstly, which can be regarded as more suitable for steganography. Then, the secret message is embedded into the enhanced cover audio with the traditional steganography, instead of into the original cover audio. The experimental results demonstrate that our generated enhanced cover audio could effectively aid traditional steganography to break the advanced DNN-based audio steganalysis.

Online publication date: Tue, 23-Nov-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Autonomous and Adaptive Communications Systems (IJAACS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com