SCF-Stega: Controllable Linguistic Steganography Based on Semantic Communications Framework

Linguistic steganography is a key information hiding technique but faces challenges like abrupt content shifts, detection risks, and high training resource demands. To address these, this paper introduces SCF-Stega, a controllable method based on Semantic Communications Framework. By using a knowledge graph to guide secret encoding and dynamically adjusting large language model outputs, SCF-Stega enhances text imperceptibility and semantic coherence. Experiments show improved text quality and strong resistance to steganalysis, without needing additional training data.