RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network

Lin, Zinan; Huang, Yudong; Wang, Jilong

doi:10.1109/tifs.2018.2806741

Cited by 70 publications

(73 citation statements)

References 35 publications

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“…There are also several attempts to apply deep learning method to steganalysis of VoIP. Lin et al [30] found there are four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data. Thus, to extract those correlation features, they propose the codeword correlation model, which is based on recurrent neural network (RNN).…”

Section: B Deep Learning Based Steganalysis Methods In Voipmentioning

confidence: 99%

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

Yang

Bao

et al. 2020

Information and Communications Security

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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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Section: B Deep Learning Based Steganalysis Methods In Voipmentioning

confidence: 99%

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

Yang

Bao

et al. 2020

Information and Communications Security

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“…Correlations between different frames are crucial in speech steganalysis [7]. Here, we adopt Multi-head self attention [9] which has recently achieved remarkable performance in modeling complicated relations between different codewords.…”

Section: Correlation Extraction Layermentioning

confidence: 99%

“…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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“…Recently, Huang et al [28] improved the Voice Activity Detection (VAD) algorithm to keep the VAD result invariant after steganography and modified several types of parameter bits in inactive frames in G.723.1 with 6.3 kbit/s mode to embed the secret messages, whose steganographic bandwidth can reach up to 101 bits per frame. Lin [29] proposed another improved VAD algorithm to keep the VAD result unchanged after steganography and extended Huang's method to 5.3 kbit/s mode.As for VoIP steganalysis, there is no universal detection method currently, but some effective steganalysis methods [30][31][32][33][34][35][36][37] have been proposed to detect steganographic algorithms, which modify specific encoding parameters. For example, Li et al [30] pointed out that the codeword of LPC became asymmetrical after embedding secret messages and proposed a quantization codework correlation network model to detect LPC-based steganography, which has a good detection performance even in short sample length.…”

mentioning

confidence: 99%

“…For example, Li et al [30] pointed out that the codeword of LPC became asymmetrical after embedding secret messages and proposed a quantization codework correlation network model to detect LPC-based steganography, which has a good detection performance even in short sample length. Lin et al [31] first introduced the recurrent neural network (RNN) into steganalysis and designed a two-layer network to detect the LPC-based steganography. The experimental results show that Lin's method [31] has better detection performance than Li's method [30] and can achieve a good detection accuracy when the sample length is only 0.1 s at the embedding rate of 100%.…”

mentioning

confidence: 99%

Steganalysis of Inactive Voice-Over-IP Frames Based on Poker Test

et al. 2018

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This paper concentrates on the detection of steganography in inactive frames of low bit rate audio streams in Voice over Internet Protocol (VoIP) scenarios. Both theoretical and experimental analyses demonstrate that the distribution of 0 and 1 in encoding parameter bits becomes symmetric after a steganographic process. Moreover, this symmetry affects the frequency of each subsequence of parameter bits, and accordingly changes the poker test statistical features of encoding parameter bits. Employing the poker test statistics of each type of encoding parameter bits as detection features, we present a steganalysis method based on a support vector machine. We evaluate the proposed method with a large quantity of speech samples encoded by G.723.1 and compare it with the entropy test. The experimental results show that the proposed method is effective, and largely outperforms the entropy test in any cases.

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RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network

Cited by 70 publications

References 35 publications

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

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

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

Steganalysis of Inactive Voice-Over-IP Frames Based on Poker Test

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