AHCM: Adaptive Huffman Code Mapping for Audio Steganography Based on Psychoacoustic Model

Most current audio steganographic methods are content non-adaptive which have poor security and low embedding capacity. This paper proposes a generalized adaptive Huffman code mapping (AHCM) framework for obtaining higher secure payload. To avoid the frame-offset effect of audio codec, we firstly establish a distortion-limited suppressible code space, which realizes data embedding by using equal-length entropy codes. Furthermore, a stego key is used to dynamically built Huffman code mapping of each frame for improving acoustic imperceptibility and statistical undetectability. We then consider integrating psychoacoustic model (PAM) of intra-frame with frame-level perceptual distortion (FPD) of inter-frame to obtain minimized total distortion. Lastly, we present an implementation of the proposed AHCM framework on MP3 audios. A distortion function based on the PAM and an optimal steganographic frame path are respectively devised for adaptively embedding via employing syndrome-trellis codes. Experimental results demonstrate that our approach is indeed able to achieve higher secure steganographic capacity and better acoustic concealment. The detection accuracy of 320kbps-mp3 datasets is lower than 65% when the embedding payload reaches 11kbps, which is decreased by 11:8% ~ 13:4% than stat-of-the-art steganographic methods.