Open Access

ARTICLE

E-SWAN: Efficient Sliding Window Analysis Network for Real-Time Speech Steganography Detection

Kening Wang^1,#, Feipeng Gao^2,#, Jie Yang^1,2,*, Hao Zhang¹

1 School of Engineering and Technology, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
2 College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, 311300, China

* Corresponding Author: Jie Yang. Email:
# Kening Wang and Feipeng Gao contributed equally to this work

Computers, Materials & Continua 2025, 82(3), 4797-4820. https://doi.org/10.32604/cmc.2025.060042

Received 22 October 2024; Accepted 20 December 2024; Issue published 06 March 2025

Abstract

With the rapid advancement of Voice over Internet Protocol (VoIP) technology, speech steganography techniques such as Quantization Index Modulation (QIM) and Pitch Modulation Steganography (PMS) have emerged as significant challenges to information security. These techniques embed hidden information into speech streams, making detection increasingly difficult, particularly under conditions of low embedding rates and short speech durations. Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals. To address these challenges, this paper proposes an Efficient Sliding Window Analysis Network (E-SWAN), a novel deep learning model specifically designed for real-time speech steganalysis. E-SWAN integrates two core modules: the LSTM Temporal Feature Miner (LTFM) and the Convolutional Key Feature Miner (CKFM). LTFM captures long-range temporal dependencies using Long Short-Term Memory networks, while CKFM identifies local spatial variations caused by steganographic embedding through convolutional operations. These modules operate within a sliding window framework, enabling efficient extraction of temporal and spatial features. Experimental results on the Chinese CNV and PMS datasets demonstrate the superior performance of E-SWAN. Under conditions of a ten-second sample duration and an embedding rate of 10%, E-SWAN achieves a detection accuracy of 62.09% on the PMS dataset, surpassing existing methods by 4.57%, and an accuracy of 82.28% on the CNV dataset, outperforming state-of-the-art methods by 7.29%. These findings validate the robustness and efficiency of E-SWAN under low embedding rates and short durations, offering a promising solution for real-time VoIP steganalysis. This work provides significant contributions to enhancing information security in digital communications.

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Keywords

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