CNN-based Steganalysis of MP3 Steganography in the Entropy Code Domain

Wang, Yuntao; Yang, Kun; Yi, Xiaowei; Zhao, Xianfeng; Xu, Zhoujun

doi:10.1145/3206004.3206011

Cited by 30 publications

(17 citation statements)

References 6 publications

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“…The approach uses LSF (line spectral frequencies) parameters as a cue of audio type. Wang et al [29] presents an effective steganalytic scheme based on CNN for detecting MP3 steganography in the entropy code domain. The above all focused on static audio file and can't be directly applied to stream media carrier.…”

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

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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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

Lecture Notes in Computer Science

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“…[134] proposed a method based on Deep Residual Network for steganalysis which uses the spectrogram as the main feature to detect steganography schemes in different embedding domains for AAC and MP3. In [135], a CNN based steganalysis method was proposed for MP3 Steganography in the Entropy Code Domain. Recently, deep RNN models were employed for DNA steganography by [136].…”

Section: H Deep Learning For Steganalysis and Steganographymentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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“…In this paper, we use a recently proposed general steganalysis method that takes the quantified modified DCT (QMDCT) coefficients matrix as the input of a convolutional neural network (CNN) [31] to analyze if the audio signal have been embedded hidden information. The CNN model is trained with the default configuration and dataset in [31]. Then the 200 original and stego audios are analyzed by the model.…”

Section: Steganalysismentioning

confidence: 99%

Adversarial Audio: A New Information Hiding Method and Backdoor for DNN-based Speech Recognition Models

Kong,

Zhang

2019

Preprint

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Audio is an important medium in people's daily life, hidden information can be embedded into audio for covert communication. Current audio information hiding techniques can be roughly classed into time domain-based and transform domain-based techniques. Time domain-based techniques have large hiding capacity but low imperceptibility. Transform domain-based techniques have better imperceptibility, but the hiding capacity is poor. This paper proposes a new audio information hiding technique which shows high hiding capacity and good imperceptibility. The proposed audio information hiding method takes the original audio signal as input and obtains the audio signal embedded with hidden information (called stego audio) through the training of our private automatic speech recognition (ASR) model. Without knowing the internal parameters and structure of the private model, the hidden information can be extracted by the private model but cannot be extracted by public models. We use four other ASR models to extract the hidden information on the stego audios to evaluate the security of the private model. The experimental results show that the proposed audio information hiding technique has a high hiding capacity of 48 cps with good imperceptibility and high security. In addition, our proposed adversarial audio can be used to activate an intrinsic backdoor of DNN-based ASR models, which brings a serious threat to intelligent speakers.

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CNN-based Steganalysis of MP3 Steganography in the Entropy Code Domain

Cited by 30 publications

References 6 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

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Adversarial Audio: A New Information Hiding Method and Backdoor for DNN-based Speech Recognition Models

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