TY  - EJOU
AU  - Wang, Kening 
AU  - Gao, Feipeng 
AU  - Yang, Jie 
AU  - Zhang, Hao 

TI  - E-SWAN: Efficient Sliding Window Analysis Network for Real-Time Speech Steganography Detection
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Steganalysis; speech; convolutional sliding window; deep learning

DO  - 10.32604/cmc.2025.060042