Content-Aware Robust JPEG Steganography for Lossy Channels Using LPCNet

Most robust steganographic methods pursue insignificantly zero-bit error rate of hidden messages for ensuring the reliability of communication. Cover JPEG images are required to recompress many times for enhancing the robustness, that reduces the security. In this letter, we propose a content-aware steganographic scheme for hiding speech signals into JPEG images by utilizing the redundancy of speech signals. Firstly, we design a steganographic communication model that combines speech coding with embedding process by using embedded redundancy of speech messages. It is suitable for all lossy channels. Secondly, for improving the robustness of speech signals under a given embedding rate, we propose a content-aware protection method by exploiting different effects of speech coding parameters on speech quality after transcoding. Finally, an optimized linear prediction net (LPCNet) model is implemented to improve the end-to-end quality of speech signals. Compared with existing algorithms, experimental results show that the end-to-end quality of embedding speech is improved by 95% and the transmission efficiency is raised by 2.16 times. Meanwhile, our scheme can resist the steganalysis attack based on the JPEG recompression feature.