Co-Stega: Collaborative Linguistic Steganography for the Low Capacity Challenge in Social Media

Social media platforms, with their extensive and real-time text data, are important application environments for linguistic generative steganography. However, the fragmented and context-constrained nature of social media text leads to a notably low capacity for hiding messages, making linguistic steganography impractical in real social media platforms. More frustratingly, even for high-capacity linguistic generative steganography, the upper bound of capacity is limited to a low level when required to meet a slightly strict security level under Cachin's model. To overcome the low capacity challenge, we identified an indicator of capacity that is independent of any specific steganography method to analyze the origins of this challenge, then we proposed a novel linguistic steganography framework named Collaborative Steganography (Co-Stega). Co-Stega utilizes existing texts and contextual relevance between texts in social media, collaboratively embedding secret messages in an existing text and its contextually related text via efficient retrieval and generation respectively. Additionally, we proposed an innovative and simple technique called "Entropy Enhancement Strategy", which effectively increases the entropy of generated text, thereby enhancing capacity further. Our evaluation shows that Co-Stega significantly improves capacity and maintains text quality, making it a valuable extension for linguistic generative steganography in social media platforms.