Fast Steganalysis Method for VoIP Streams

TL;DR

This paper introduces a rapid and accurate steganalysis method for VoIP streams that leverages correlation analysis and a lightweight neural network, enabling real-time detection with minimal processing time.

Contribution

The paper presents a novel, fast steganalysis approach for VoIP streams using correlation features and a simple neural network, enhanced by knowledge distillation for improved performance.

Findings

Achieves state-of-the-art detection accuracy.

Processing time is approximately 0.05% of the sample duration.

Effective for online steganography monitoring.

Abstract

In this letter, we present a novel and extremely fast steganalysis method of Voice over IP (VoIP) streams, driven by the need for a quick and accurate detection of possible steganography in VoIP streams. We firstly analyzed the correlations in carriers. To better exploit the correlation in code-words, we mapped vector quantization code-words into a semantic space. In order to achieve high detection efficiency, only one hidden layer is utilized to extract the correlations between these code-words. Finally, based on the extracted correlation features, we used the softmax classifier to categorize the input stream carriers. To boost the performance of this proposed model, we incorporate a simple knowledge distillation framework into the training process. Experimental results show that the proposed method achieves state-of-the-art performance both in detection accuracy and efficiency. In particular, the processing time of this method on average is only about 0.05\% when sample length is as short as 0.1s, attaching strong practical value to online serving of steganography monitor.