The coverless text steganography has become a research hotspot in the field of information hiding because no modification to a text carrier will improve the concealment of steganography. However, compared with images, audios, videos, or other carriers, a plain text has less redundancy for modification, so the text steganographic embedding rate is usually lower, and semantic anomalies are more easily produced once it is modified or generated. This paper analyzes the parity of Chinese characters' stroke number and its statistical characteristic, and reach the conclusion that this parity feature exhibits a uniform distribution and it exists in every character. These indicate that the parity can be employed to express a binary digit and its combination can represent a binary digit string. In the light of the above analyses, we propose a new coverless plain text steganography based on the parity of Chinese characters' stroke number and extend it to further improve the embedding rate. By exploiting the space mapping concept, this method initializes a binary search tree based on the texts from the Internet and searches the corresponding texts according to the secret binary digit string generated from a secret. Thus, a secret will be mapped into a list of the corresponding uniform resource locators. That is, our proposed methods only employ the existing uniform resource locators to convey a secret. Compared with the existing text steganography techniques, our methods not only have high-embedding rate and good concealment but also can improve the embedding capacity without impact on the concealment, which eliminates the contradiction between the embedding capacity/rate and security, because no modification or additional information is generated. In addition, these methods have a prominent search success rate, and they can be extended to other language features as well as to other languages.INDEX TERMS Character features, coverless text steganography, information hiding, the strokes of Chinese characters.
Traditional audio steganography by cover modification causes changes to the cover features during the embedding of a secret, which is easy to detect with emerging neural-network steganalysis tools. To address the problem, this paper proposes a coverless audio-steganography model to conceal a secret audio. In this method, the stego-audio is directly synthesized by our model, which is based on the WaveGAN framework. An extractor is meticulously designed to reconstruct the secret audio, and it contains resolution blocks to learn the different resolution features. The method does not perform any modification to an existing or generated cover, and as far as we know, this is the first directly generated stego-audio. The experimental results also show that it is difficult for the current steganalysis methods to detect the existence of a secret in the stego-audio generated by our method because there is no cover audio. The MOS metric indicates that the generated stego-audio has high audio quality. The steganography capacity can be measured from two perspectives, one is that it can reach 50% of the stego-audio from the simple size perspective, the other is that 22–37 bits can be hidden in a two-second stego-audio from the semantic. In addition, we prove using spectrum diagrams in different forms that the extractor can reconstruct the secret audio successfully on hearing, which guarantees complete semantic transmission. Finally, the experiment of noise impacts on the stego-audio transmission shows that the extractor can still completely reconstruct the semantics of the secret audios, which indicates that the proposed method has good robustness.
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