Hierarchical Graph Neural Network for Compressed Speech Steganalysis

• URL: http://arxiv.org/abs/2507.21591v1
• Date: Tue, 29 Jul 2025 08:46:54 GMT
• Title: Hierarchical Graph Neural Network for Compressed Speech Steganalysis
• Authors: Mustapha Hemis, Hamza Kheddar, Mohamed Chahine Ghanem, Bachir Boudraa,
• Abstract summary: This paper presents the first application of a Graph Neural Network (GNN) for steganalysis of compressed voice over IP (VoIP) speech streams.<n>The method involves straightforward graph construction from VoIP streams and employs GraphSAGE to capture hierarchical steganalysis information.<n>It achieves detection accuracy exceeding 98 percent even for short 0.5 second samples, and 95.17 percent accuracy under challenging conditions with low embedding rates.
• Score: 0.4017708359820078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Steganalysis methods based on deep learning (DL) often struggle with computational complexity and challenges in generalizing across different datasets. Incorporating a graph neural network (GNN) into steganalysis schemes enables the leveraging of relational data for improved detection accuracy and adaptability. This paper presents the first application of a Graph Neural Network (GNN), specifically the GraphSAGE architecture, for steganalysis of compressed voice over IP (VoIP) speech streams. The method involves straightforward graph construction from VoIP streams and employs GraphSAGE to capture hierarchical steganalysis information, including both fine grained details and high level patterns, thereby achieving high detection accuracy. Experimental results demonstrate that the developed approach performs well in uncovering quantization index modulation (QIM)-based steganographic patterns in VoIP signals. It achieves detection accuracy exceeding 98 percent even for short 0.5 second samples, and 95.17 percent accuracy under challenging conditions with low embedding rates, representing an improvement of 2.8 percent over the best performing state of the art methods. Furthermore, the model exhibits superior efficiency, with an average detection time as low as 0.016 seconds for 0.5-second samples an improvement of 0.003 seconds. This makes it efficient for online steganalysis tasks, providing a superior balance between detection accuracy and efficiency under the constraint of short samples with low embedding rates.

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