Calibrated steganalysis of mp3stego in multi-encoder scenario

TL;DR

This paper addresses the gap in mp3 steganalysis by analyzing encoder variations and introducing a calibration method based on quantization steps, improving detection robustness across different encoders.

Contribution

It presents a new analysis of encoder differences and proposes a calibrated feature set using quantization step estimation for more effective mp3 steganalysis.

Findings

Encoder differences can be classified with four features.

Calibrated features improve steganalysis performance.

Quantization step-based calibration enhances robustness.

Abstract

Comparing popularity of mp3 and wave with the amount of works published on each of them shows mp3 steganalysis has not found adequate attention. Furthermore, investigating existing works on mp3 steganalysis shows that a major factor has been overlooked. Experimenting with different mp3 encoders shows there are subtle differences in their outputs. This shows that mp3 standard has been implemented in dissimilar fashions, which in turn could degrade performance of steganalysis if it is not addressed properly. Additionally, calibration is a powerful technique which has not found its true potential for mp3 steganalysis. This paper tries to fill these gaps. First, we present our analysis on different encoders and show they can be classified quite accurately with only four features. Then, we propose a new set of calibrated features based on quantization step. To that end, we show quantization step is a band limited signal and steganography noise affects its high frequency components more prominently. By applying a low pass filter on quantization steps, we arrive at an estimation of quantization step, which in turn is used for calibrating the features.