Frame-level steganalysis of QIM steganography in compressed speech based on multi-dimensional perspective of codeword correlations

Wang, Miao; Li, Songbin; Liu, Peng; Huang, Yudong; Yan, Qiandong; Wang, Jingang; Zhang, Cheng

doi:10.1007/s12652-021-03608-9

Cited by 5 publications

(2 citation statements)

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“…(Note that we refer to this model as DRCM in this paper.) Meanwhile, Wei et al [13] also used codeword embedding and Bi-LSTM to propose a QIM-oriented speech steganalysis method, which can achieve frame-level speech steganography detection.…”

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%