Steganalysis of AAC using calibrated Markov model of adjacent codebook
Yanzhen Ren, Qiaochu Xiong, Lina Wang
Abstract
AAC(Advanced Audio Coding) is the most popular audio compression standard and used widely in recent years. The steganography schemes of AAC emerged gradually. This paper presents a novel steganalysis method to attack the steganography of Huffman codebook, which hide information by modifying the codebook of each scale factor band(SFB), and have good imperceptivity and security. Based on the correlation of neighboring SFBs' codebook, the paper proposes to extract the Markov transition probability of adjacent SFBs' codebook as steganalysis feature, and adopt calibration to improve the accuracy. Extensive experiments demonstrate the effectiveness of the proposed methods. To the best of our knowledge, this piece of work is the first one to detect AAC steganography of Huffman codebook.
BibTeX
@inproceedings{icassp2016_steganalysisofaa,
title = {Steganalysis of AAC using calibrated Markov model of adjacent codebook},
author = {Yanzhen Ren and Qiaochu Xiong and Lina Wang},
booktitle = {ICASSP 2016},
year = {2016}
}