Nowadays, social media has become the preferred communication platform for
web users but brought security threats. Linguistic steganography hides secret
data into text and sends it to the intended recipient to realize covert
communication. Compared to edit-based linguistic steganography,
generation-based approaches largely improve the payload capacity. However,
existing methods can only generate stego text alone. Another common behavior in
social media is sending semantically related image-text pairs. In this paper,
we put forward a novel image captioning-based stegosystem, where the secret
messages are embedded into the generated captions. Thus, the semantics of the
stego text can be controlled and the secret data can be transmitted by sending
semantically related image-text pairs. To balance the conflict between payload
capacity and semantic preservation, we proposed a new sampling method called
Two-Parameter Semantic Control Sampling to cutoff low-probability words.
Experimental results have shown that our method can control diversity, payload
capacity, security, and semantic accuracy at the same time.