Open Access

ARTICLE

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

Cuilin Wang¹, Yuling Liu^1,*, Yongju Tong¹, Jingwen Wang²

1 College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
2 Department of Computer Science, Elizabethtown College, PA, 17022, USA

* Corresponding Author: Yuling Liu. Email:

Computers, Materials & Continua 2021, 69(1), 1375-1390. https://doi.org/10.32604/cmc.2021.017950

Received 18 February 2021; Accepted 13 April 2021; Issue published 04 June 2021

Abstract

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.

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Citations