Text steganography has received a lot of attention in the application of covert communication. How to ensure desirable capacity and imperceptibility has become a key issue in text steganography. There are two typical approaches, i.e., text-selection-based steganography and text-generation-based steganography. However, the text-selection-based approaches generally have the very low hidden capacity and are not applicable in practical scenarios. Although the text-generation-based approaches can embed secret messages with higher capacity during text generation, they are prone to semantic incoherence and semantic errors when generating long texts. To address the abovementioned issues, this article proposes a novel text steganography based on long readable text generation. It first determines the topic of the stego-text according to the scenarios of the communication parties. Then, the plug and play language model (PPLM) is explored to generate the long readable stego-text conforming to the topic with semantic coherency. A given secret message is hidden during text generation by selecting proper words in an established embeddable candidate word pool (ECWP). Establishing the ECWP prevents the language model (LM) from selecting words with low probability in the text generation, thereby avoiding the generation of low-quality or even grammatically incorrect stego-text. Experimental results show that the proposed approach significantly increases hidden capacity while maintaining good imperceptibility compared with the existing approaches.

Yi Cao

Zhili Zhou

Chinmay Chakraborty

Meimin Wang

Q. M. Jonathan Wu

Xingming Sun

Keping Yu

IEEE Transactions on Computational Social Systems

IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.

The IEEE Transactions on Computational Social Systems focuses on modeling, simulation, analysis, and understanding of social systems from quantitative and/or computational perspectives. "Systems" encompass man-man, man-machine, and machine-machine organizations, adversarial situations, as well as social media structures and their dynamics. Specifically, the journal publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. It also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, computational behavior modeling, and their applications.

《IEEE计算社会系统汇刊》专注于从定量和/或计算的角度对社会系统进行建模、仿真、分析和理解。这里的“系统”包括人际关系、人与机器、机器与机器之间的组织和对抗情境，以及社交媒体结构及其动态。具体而言，该期刊发表关于社会系统动态建模、将社会文化和行为方面纳入计算建模的方法论、社会系统行为和结构分析，以及社会系统建模和仿真的范式的文章。期刊还涵盖社交网络动态、社会智能与认知、社会系统设计与架构、社会文化建模与表现、计算行为建模及其应用等主题的文章。

Generative Steganography Based on Long Readable Text Generation

This article proposes a novel text steganography based on long readable text generation that significantly increases hidden capacity while maintaining good imperceptibility compared with the existing approaches.

Category	Quartile
计算机科学	2
计算机科学, 计算机信息系统	2
计算机科学, 计算机控制论	3

Generative Steganography Based on Long Readable Text Generation

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Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	1