Hierarchical Representation Network for Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

Yang, Hao; Yang, Zhongliang; Bao, YongJian; Huang, Yudong

doi:10.1007/978-3-030-41579-2_45

Cited by 14 publications

(6 citation statements)

References 33 publications

(37 reference statements)

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“…Experiments results validated that their method outperforms the existing stateof-the-art methods. In 2019 and 2020, Hao et al [22,37] successively proposed hierarchical representation network and multihead attention-based network to extract correlation features for QIM steganalysis. Both methods significantly improve the best result especially in detecting both short and low embedded speech samples.…”

Section: Related Workmentioning

confidence: 99%

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

Guo

Yang

Shuai

et al. 2021

Security and Communication Networks

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Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. However, previous studies lacked mathematical modeling of the correlation between codewords, and there is still room for improvement in steganalysis for small-sized and low embedding rate samples. To deal with the challenge, we use Bayesian networks to measure different types of correlations between codewords in linear prediction code and present F3SNet—a four-step strategy: embedding, encoding, attention, and classification for quantization index modulation steganalysis of compressed speech based on the hierarchical attention network. Among them, embedding converts codewords into high-density numerical vectors, encoding uses the memory characteristics of LSTM to retain more information by distributing it among all its vectors, and attention further determines which vectors have a greater impact on the final classification result. To evaluate the performance of F3SNet, we make a comprehensive comparison of F3SNet with existing steganography methods. Experimental results show that F3SNet surpasses the state-of-the-art methods, particularly for small-sized and low embedding rate samples.

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Section: Related Workmentioning

confidence: 99%

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

Guo

Yang

Shuai

et al. 2021

Security and Communication Networks

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show abstract

“…Experiments results validated that their method outperforms the existing state-of-the-art methods. In 2019 and 2020, Hao et al [21,22]…”

Section: Related Workmentioning

confidence: 99%

“…In 2019, Chen et al proposed a steganalytic scheme by combining RNN and Convolutional Neural Network (CNN) for FCB steganography. In 2019 and 2020, Hao et al [21,22] successively proposed hierarchical representation Network and multi-head attentionbased network to extract correlation features for QIM steganalysis. However, sequence coding based on CNN or RNN is still a local coding method, and it models the local dependency of input information.…”

Section: Introductionmentioning

confidence: 99%

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

Guo¹,

Yang²,

Huang³

2021

Preprint

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Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. However, previous studies lacked mathematical description and modeling of the correlation between codewords, and there is still room for improvement in steganalysis for small-sized and low embedding rates sample. To deal with the challenge, We use Bayesian networks to measure different types of correlations between codewords in linear prediction code and present F3SNet -a four-step strategy: Embedding, Encoding, Attention and Classification for quantizaition index modulation steganalysis of compressed speech based on Hierarchical Attention Network. Among them, Embedding converts codewords into high-density numerical vectors, Encoding uses the memory characteristics of LSTM to retain more information by distributing it among all its vectors and Attention further determines which vectors have a greater impact on the final classification result. To evaluate the performance of F3SNet, we make comprehensive comparison of F3SNet with existing steganography methods. Experimental results show that F3SNet surpasses the state-of-the-art methods, particularly for small-sized and low embedding rate samples.

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“…In order to validate the performance of our model, we compare with other state-of-the-art methods: QCCN [16], RNN-SM [7], R-CNN [8], and HRN [17]. Metrics used in experiments are detection accuracy and inference time.…”

Section: Experimental Settingsmentioning

confidence: 99%

FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis Of VoIP Stream Via Multi-Head Attention

Yang

Bao

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to their highly parallelizable computation and flexibility in modeling correlation in sequence, to tackle steganalysis problem of Quantization Index Modulation (QIM) based steganography in compressed VoIP stream. We design a light-weight neural network named Fast Correlation Extract Model (FCEM) only based on a variant of attention called multi-head attention to extract correlation features from VoIP frames. Despite its simple form, FCEM outperforms complicated Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) models on both prediction accuracy and time efficiency. It significantly improves the best result in detecting both low embedded rates and short samples recently. Besides, the proposed model accelerates the detection speed as twice as before when the sample length is as short as 0.1s, making it a excellent method for online services.

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Hierarchical Representation Network for Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

Cited by 14 publications

References 33 publications

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis Of VoIP Stream Via Multi-Head Attention

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