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

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

doi:10.1109/icassp40776.2020.9054361

Cited by 16 publications

(16 citation statements)

References 17 publications

(21 reference statements)

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“…irdly, as described above, for steganography based on compressed speech, researchers have successively developed a variety of steganalysis methods. Among them, the typical algorithms are IDC [12], QCCN [13], RNN-SM [20], and FCEM [22]. Below we will compare the performance of these state-of-the-art algorithms and F3SNet using different lengths and different embedding rates.…”

Section: Methodsmentioning

confidence: 99%

“…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%

“…For example, model #2 only considers a single-layer attention structure, model #3 does not use a LSTM layer, and model #4 does not use an embedding layer. In addition, model #5 and #6 are the two deep learning models proposed before [20,22], and both are compared here.…”

Section: Comparison With Different Networkmentioning

confidence: 99%

“…(3) To evaluate the performance of F3SNet, we perform comprehensive experiments on detection accuracy (ACC), false positive rate (FPR), and false negative rate (FNR) of the algorithm under different lengths and different embedding rates. Furthermore, we compare F3SNet with several existing algorithms, such as IDC [12], QCCN [13], RNN-SM [20], and FCEM [22] methods under different embedding rates and different lengths. e experimental results show that our algorithm is superior to other state-ofthe-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With Different Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Our work is motivated by the observation that the interactions between words in text are important for steganalysis and multi-head self-attention has great potential to model these interactions [9,10]. us, we propose a neural steganalysis approach with multi-head self-attention.…”

Section: Introductionmentioning

confidence: 99%

Neural Linguistic Steganalysis via Multi-Head Self-Attention

Jiao

Wang

Zhang

et al. 2021

Journal of Electrical and Computer Engineering

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Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach.

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

Yang

Bao

et al. 2020

Lecture Notes in Computer Science

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With the Volume of Voice over IP (VoIP) traffic rises shapely, more and more VoIP-based steganography methods have emerged in recent years, which poses a great threat to the security of cyberspace. Low bit-rate speech codecs are widely used in the VoIP application due to its powerful compression capability. QIM steganography makes it possible to hide secret information in VoIP streams. Previous research mostly focus on capturing the inter-frame correlation or inner-frame correlation features in code-words but ignore the hierarchical structure which exists in speech frame. In this paper, motivated by the complex multiscale structure, we design a Hierarchical Representation Network to tackle the steganalysis of QIM steganography in low-bitrate speech signal. In the proposed model, Convolution Neural Network (CNN) is used to model the hierarchical structure in the speech frame, and three level of attention mechanisms are applied at different convolution block, enabling it to attend differentially to more and less important content in speech frame. Experiments demonstrated that the steganalysis performance of the proposed method can outperforms the state-of-the-art methods especially in detecting both short and low embeded speech samples. Moreover, our model needs less computation and has higher time efficiency to be applied to real online services.

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

Cited by 16 publications

References 17 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

Neural Linguistic Steganalysis via Multi-Head Self-Attention

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

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