Abstract
It is a challenge to conduct natural language steganography on Online interactive platforms such as photo-sharing websites since the stego texts should be consistent with the content of the images. In this paper, a novel natural language steganographic framework based on an end-to-end generative network is proposed. A Convolution Neural Network (CNN) combined with Long Short-Term Memory (LSTM) is trained to generate stego descriptions. Word by Word Hiding (WWH) and Sentence by Sentence Hiding (SSH) schemes are proposed to achieve various embedding capacity under the premise of sharing model between the sender and the receiver. Furthermore, a blind extraction scheme called Hash Hiding (HH) is proposed in case that the model is unavailable for data extraction. Comparative experiments show the superiority of the proposed framework. It is verified that the proposed framework is an effective carrier-less steganographic framework with competitive embedding capacity, considerable text quality, and good reversibility.
Published Version
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