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

arXiv: https://arxiv.org/abs/1710.01230 · HTML: https://mlsanity.com/paper/1710.01230/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting](https://mlsanity.com/paper/2308.11197/) (2023)

## Abstract (arXiv)

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.