Separable Convolution Network With Dual-Stream Pyramid Enhanced Strategy for Speech Steganalysis

Qiu, Yiqin; Tian, Hui; Li, Haizhou; Chang, Chin‐Chen; Vasilakos, Athanasios V.

doi:10.1109/tifs.2023.3269640

Cited by 4 publications

(2 citation statements)

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“…For instance, Tian et al [14] proposed a speech steganalysis model using feature fusion and LSTM to detect Adaptive-Codebook-based steganography. Qiu et al [15] designed a novel separable convolution network with a dual-stream pyramid-enhanced strategy to detect Fixed-Codebook-based steganography. For the general detection of multiple steganography methods, Hu et al [16] proposed a novel deep learning model named Steganalysis Feature Fusion Network.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, speech steganalysis [5,6], whose primary purpose is to detect the existence of hidden messages in speech signals, has been attracting increasing attention in recent years. Particularly, with the development of artificial intelligence, many well-performing speech steganalysis methods based on deep learning [7][8][9][10][11][12][13][14][15][16][17][18] have been proposed, since deep learning can capture the subtle differences between the steganographic and cover samples.…”

Section: Introductionmentioning

confidence: 99%

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FedSpy: A Secure Collaborative Speech Steganalysis Framework Based on Federated Learning

et al. 2023

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Deep learning brings the opportunity to achieve effective speech steganalysis in speech signals. However, the speech samples used to train speech steganalysis models (i.e., steganalyzers) are usually sensitive and distributed among different agencies, making it impractical to train an effective centralized steganalyzer. Therefore, in this paper, we present an effective framework, named FedSpy, using federated learning, which enables multiple agencies to securely and jointly train the speech steganalysis models without sharing their speech samples. FedSpy is a flexible and extensible framework that can work effectively in conjunction with various deep-learning-based speech steganalysis methods. We evaluate the performance of FedSpy by detecting the most widely used Quantization Index Modulation-based speech steganography with three state-of-the-art deep-learning-based steganalysis methods representatively. The results show that FedSpy significantly outperforms the local steganalyzers and achieves good detection accuracy comparable to the centralized steganalyzer.

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Section: Related Workmentioning

confidence: 99%