An Autoregressive Time Delay Neural Network for speech steganalysis

Rekik, Siwar; Selouani, Sid‐Ahmed; Guerchi, Driss; Hamam, Habib

doi:10.1109/isspa.2012.6310612

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…In another work, Paulin et al [27] presented a new method to train Restricted Boltzmann Machines (RBMs) using Evolutionary Algorithms (EAs), where RBMs are used in the first step of a steganalysis tool for audio files and the vector they used to train the model was MFCC. S. Rekik et al [28] advocated a powerful and sophisticated classifier called Autoregressive Time Delay Neural Network (AR-TDNN). The approach uses LSF (line spectral frequencies) parameters as a cue of audio type.…”

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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“…For example, C. Paulin et al [40] first extracted mel-frequency cepstral coefficients (MFCCs) features of input audio and then used a deep belief networks (DBN) to classify them. S. Rekik et al [39] extracted the Line Spectrum Frequency (LSF) features from original audio and then used a Time Delay Neural Networks (TDNN) to detect stego-speech. These methods are performed by manually extracting the statistical characteristics of the speech signals and then using the neural network models for analysis and detection.…”

Section: B Speech Steganalysismentioning

confidence: 99%

Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows

Yang¹,

Yang²,

Hu³

et al. 2019

Preprint

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With the development and popularization of network technology, communication using Voice over IP (VoIP) has become an increasingly common means of information transmission in people's daily lives. With these emerging communication channels, more and more VoIP-based steganography methods have appeared in recent years, which pose a great threat to the security of cyberspace. Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolutional Neural Network (CNN), we propose a real-time VoIP steganalysis method which based on multi-channel convolutional sliding windows (CSW). In order to analyze the correlations between frames and different neighborhood frames in a VoIP signal, we define multichannel sliding detection windows. Within each sliding window, we design two feature extraction channels which contain multiple convolutional layers with multiple convolution kernels each layer to extract correlation features of the input signal. Then based on these extracted features, we use a forward fully connected network for feature fusion. Finally, by analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not. We designed several experiments to test the proposed model's detection ability under various conditions, including different embedding rates, different speech length, etc. Experimental results showed that the proposed model outperforms all the previous methods, especially in the case of low embedding rate, which showed state-of-the-art performance. In addition, we also tested the detection efficiency of the proposed model, and the results showed that it can achieve almost real-time detection of VoIP speech signals.

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“…Considering the difficulty of modelling temporal characteristics of audio signals, this decision becomes even harder. A special type of networks known as autoregressive time delay neural network (AR-TDNN) was proposed in [14] to address this problem. AR-TDNN has the advantage that feature extraction is not specified explicitly, but the network implements both feature extraction and classification parts of the system.…”

Section: Autoregressive Time Delay Neural Networkmentioning

confidence: 99%

Comprehensive review of audio steganalysis methods

Ghasemzadeh

Kayvanrad

2018

IET signal process.

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Recently, merging signal processing techniques with information security services has found a lot of attention. Steganography and steganalysis are among those trends. Like their counterparts in cryptology, steganography and steganalysis are in a constant battle. Steganography methods try to hide the presence of covert messages in innocuouslooking data, whereas steganalysis methods try to break steganography algorithms and reveal the existence of hidden messages. The stream nature of audio signals, their popularity, and their wide spread usage make them very good candidates for steganography. This has led to a very rich literature on both steganography and steganalysis of audio signals. This paper intends to conduct a comprehensive review of audio steganalysis methods aggregated over near fifteen years. To that end, both compressed and con-compressed methods are reviewed, and then their important details are presented in different tables. Furthermore, some of the most recent audio steganalysis methods (both non-compressed and compressed ones) are implemented and comparative analyses on their performances are conducted. Finally, the paper provides some possible directions for future researches on audio steganalysis.

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An Autoregressive Time Delay Neural Network for speech steganalysis

Cited by 14 publications

References 13 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

Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows

Comprehensive review of audio steganalysis methods

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