ALiSa: Acrostic Linguistic Steganography Based on BERT and Gibbs Sampling
Biao Yi, Hanzhou Wu, Guorui Feng, Xinpeng Zhang
Abstract
In this letter, we propose a novel linguistic steganographic method that directly conceals a token-level secret message in a seemingly-natural steganographic text generated by the off-the-shelf BERT model equipped with Gibbs sampling. Compared with all modification based linguistic steganographic methods, the proposed method does not modify a given cover text. Instead, the proposed method utilizes the secret message to directly generate the steganographic text. Compared with mainstream generation based linguistic steganographic methods, the proposed method enables the receiver to collect the tokens of the specific positions to directly constitute the secret message, without a complex decoding process and much side information shared between the sender and the receiver. Experimental results show that the proposed method can generate fluent, highly readable steganographic texts, while enjoying pretty good anti-steganalysis ability. This work has great application potential in real-time covert communication.