Multi-layer architecture for efficient steganalysis of Undermp3cover in multi-encoder scenario

TL;DR

This paper introduces a multi-layer architecture for detecting hidden messages in MP3 files, specifically targeting the UnderMp3Cover method, by leveraging joint distributions and encoder-specific features to improve detection accuracy.

Contribution

The paper proposes a novel multi-layer architecture that separately detects encoder types and performs steganalysis, outperforming traditional single-layer methods by 20.4% in accuracy.

Findings

Multi-layer architecture improves detection accuracy.

Joint distributions effectively detect subtle global gain changes.

Encoder-specific feature optimization enhances steganalysis performance.

Abstract

Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts. But, current literature has only focused on steganalysis of mp3stego. In this paper we mention some of the limitations of mp3stego and argue that UnderMp3Cover (Ump3c) does not have those limitations. Ump3c makes subtle changes only to the global gain of bitstream and keeps the rest of bitstream intact. Therefore, its detection is much harder than mp3stego. To address this, joint distributions between global gain and other fields of mp3 bit stream are used. The changes are detected by measuring the mutual information from those joint distributions. Furthermore, we show that different mp3 encoders have dissimilar performances. Consequently, a novel multi-layer architecture for steganalysis of Ump3c is proposed. In this manner, the first layer detects the encoder and the second layer performs the steganalysis job. One of advantages of this architecture is that feature extraction and feature selection can be optimized for each encoder separately. We show this multi-layer architecture outperforms the conventional single-layer methods. Comparing results of the proposed method with other works shows an improvement of 20.4% in the accuracy of steganalysis.