Loading…

GAN-GLS: Generative Lyric Steganography Based on Generative Adversarial Networks

Steganography based on generative adversarial networks (GANs) has become a hot topic among researchers. Due to GANs being unsuitable for text fields with discrete characteristics, researchers have proposed GAN-based steganography methods that are less dependent on text. In this paper, we propose a n...

Full description

Saved in:
Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2021, Vol.69 (1), p.1375-1390
Main Authors: Wang, Cuilin, Liu, Yuling, Tong, Yongju, Wang, Jingwen
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Steganography based on generative adversarial networks (GANs) has become a hot topic among researchers. Due to GANs being unsuitable for text fields with discrete characteristics, researchers have proposed GAN-based steganography methods that are less dependent on text. In this paper, we propose a new method of generative lyrics steganography based on GANs, called GAN-GLS. The proposed method uses the GAN model and the large-scale lyrics corpus to construct and train a lyrics generator. In this method, the GAN uses a previously generated line of a lyric as the input sentence in order to generate the next line of the lyric. Using a strategy based on the penalty mechanism in training, the GAN model generates non-repetitive and diverse lyrics. The secret information is then processed according to the data characteristics of the generated lyrics in order to hide information. Unlike other text generation-based linguistic steganographic methods, our method changes the way that multiple generated candidate items are selected as the candidate groups in order to encode the conditional probability distribution. The experimental results demonstrate that our method can generate high-quality lyrics as stego-texts. Moreover, compared with other similar methods, the proposed method achieves good performance in terms of imperceptibility, embedding rate, effectiveness, extraction success rate and security.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2021.017950