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

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1375-1390, 2021, DOI:10.32604/cmc.2021.017950

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.… More >

Ning Wu^1,2, Yi Yang^1,*, Lian Li¹, Zhongliang Yang³, Poli Shang⁴, Weibo Ma⁵, Zhenru Liu⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1035-1048, 2020, DOI:10.32604/cmes.2020.010450

Abstract In the field of information hiding, text is less redundant, which leads to less space to hide information and challenging work for researchers. Based on the Markov chain model, this paper proposes an improved evaluation index and onebit embedding coverless text steganography method. In the steganography process, this method did not simply take the transition probability as the optimization basis of the steganography model, but combined it with the sentence length in the corresponding nodes in the model to gauge sentence quality. Based on this, only two optimal conjunctions of the current words are retained in the method to generate… More >