Efficient Streaming Voice Steganalysis in Challenging Detection Scenarios

TL;DR

This paper presents a novel dual-view framework for detecting covert information in streaming VoIP calls, especially effective under low embedding rates and short durations, significantly improving detection accuracy and real-time performance.

Contribution

The paper introduces a dual-view VoIP steganalysis framework that enhances detection of hidden information in challenging scenarios by obfuscating features and capturing fine-grained local and global features.

Findings

Outperforms existing steganalysis methods in accuracy.

Effective in low embedding rate and short duration scenarios.

Achieves near-real-time detection performance.

Abstract

In recent years, there has been an increasing number of information hiding techniques based on network streaming media, focusing on how to covertly and efficiently embed secret information into real-time transmitted network media signals to achieve concealed communication. The misuse of these techniques can lead to significant security risks, such as the spread of malicious code, commands, and viruses. Current steganalysis methods for network voice streams face two major challenges: efficient detection under low embedding rates and short duration conditions. These challenges arise because, with low embedding rates (e.g., as low as 10%) and short transmission durations (e.g., only 0.1 second), detection models struggle to acquire sufficiently rich sample features, making effective steganalysis difficult. To address these challenges, this paper introduces a Dual-View VoIP Steganalysis Framework (DVSF). The framework first randomly obfuscates parts of the native steganographic descriptors in VoIP stream segments, making the steganographic features of hard-to-detect samples more pronounced and easier to learn. It then captures fine-grained local features related to steganography, building on the global features of VoIP. Specially constructed VoIP segment triplets further adjust the feature distances within the model. Ultimately, this method effectively address the detection difficulty in VoIP. Extensive experiments demonstrate that our method significantly improves the accuracy of streaming voice steganalysis in these challenging detection scenarios, surpassing existing state-of-the-art methods and offering superior near-real-time performance.