Generative Text Steganography via Multiple Social Network Channels Based on Transformers

Abstract

AbstractGenerative text steganography uses the conditional probability to encode the candidate words when generating tokens by language model, and then selects the corresponding word to output according to the secret message to be embedded, so as to generate stego text. The complex and open characteristics of social network provide a good camouflage environment for the transmission of stego texts, but also bring challenges: transmitting stego text through a single channel is easy to cause the destruction and loss of secret message; the speech of each social account needs to be combined with its background knowledge, so it has different language features. The existing text steganography schemes cannot solve these problems well. This paper proposes a multi-channel generative text steganography scheme in the context of social network, which hides secret message into multiple semantically natural texts, even if only a part of which can reconstruct secret message. Combined with the characteristics of social network, the bag-of-words models are used to control the topics of the stego texts in the process of text generation by language model. Two goal programming models are proposed to optimize the topic relevance and text quality of stego text. The experiment verifies the effectiveness of this scheme.KeywordsText steganographyControllable text generationLoss toleranceRobustnessImperceptibility