short-paper

Audio Steganalysis with Improved Convolutional Neural Network

Authors:

Xueyuan ZhangAuthors Info & Claims

IH&MMSec'19: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security

Pages 210 - 215

https://doi.org/10.1145/3335203.3335736

Published: 02 July 2019 Publication History

Abstract

Deep learning, especially the convolutional neural network (CNN), has enjoyed significant success in many fields, e.g., image recognition. Recently, CNN has successfully applied to multimedia steganalysis. However, the detection performance is still unsatisfactory. In this work, we propose an improved CNN-based method for audio steganalysis. Specifically, a special convolutional layer is first carefully designed, which could capture the minor steganographic noise. Then, a truncated linear unit is adapted to activate the output of shallow convolutional layer. In addition, we employ the average pooling to minimize the over-fitting risk. Finally, a parameter transfer strategy is adopted, aiming to boost the detection performance for the low embedding-rate cases. The experimental results evaluated on 30,000 audio clips verify the effectiveness of our method for a variety of embedding rates. Compared with the existing CNN-based steganalysis methods, our proposed method could achieve superior performance. To facilitate the reproducible research, the source code will be released at GitHub.

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Cited By

Lawal AOwolafe OThompson A(2024)Audio Steganalysis Using Fractal Dimension and Convolutional Neural Network (CNN) ModelEmerging Technologies and Security in Cloud Computing10.4018/979-8-3693-2081-5.ch015(339-362)Online publication date: 14-Feb-2024
https://doi.org/10.4018/979-8-3693-2081-5.ch015
Zhang XChen KDing JYang YZhang WYu N(2024)Provably Secure Public-Key Steganography Based on Elliptic Curve CryptographyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.336121919(3148-3163)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3361219
Li SWang JLiu PShi K(2024)SANet: A Compressed Speech Encoder and Steganography Algorithm Independent Steganalysis Deep Neural NetworkIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.333766732(680-690)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TASLP.2023.3337667
Show More Cited By

Index Terms

Audio Steganalysis with Improved Convolutional Neural Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Security and privacy
  1. Security services
    1. Authentication

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Published In

cover image ACM Conferences

IH&MMSec'19: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security

July 2019

249 pages

ISBN:9781450368216

DOI:10.1145/3335203

General Chairs:
Rémi Cogranne
Troyes University of Technology, France
,
Luisa Verdoliva
University Federico II of Naples, Italy
,
Program Chairs:
Siwei Lyu
University at Albany, New York, USA
,
Juan Pastoriza
Ecole polytechnique fédérale de Lausanne, Switzerland
,
Xinpeng Zhang
Shanghai University, China

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 July 2019

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Short-paper

Funding Sources

National Natural Science Foundation of China
Ningbo Natural Science Foundation
Zhejiang Natural Science Foundation

Conference

IH&MMSec '19

Sponsor:

SIGMM

IH&MMSec '19: ACM Information Hiding and Multimedia Security Workshop

July 3 - 5, 2019

Paris, France

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Citations

Cited By

Lawal AOwolafe OThompson A(2024)Audio Steganalysis Using Fractal Dimension and Convolutional Neural Network (CNN) ModelEmerging Technologies and Security in Cloud Computing10.4018/979-8-3693-2081-5.ch015(339-362)Online publication date: 14-Feb-2024
https://doi.org/10.4018/979-8-3693-2081-5.ch015
Zhang XChen KDing JYang YZhang WYu N(2024)Provably Secure Public-Key Steganography Based on Elliptic Curve CryptographyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.336121919(3148-3163)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3361219
Li SWang JLiu PShi K(2024)SANet: A Compressed Speech Encoder and Steganography Algorithm Independent Steganalysis Deep Neural NetworkIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.333766732(680-690)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TASLP.2023.3337667
Kheddar HHemis MHimeur YMegías DAmira A(2024)Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directionsNeurocomputing10.1016/j.neucom.2024.127528581(127528)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127528
Li JWang KJia X(2023)A Coverless Audio Steganography Based on Generative Adversarial NetworksElectronics10.3390/electronics1205125312:5(1253)Online publication date: 5-Mar-2023
https://doi.org/10.3390/electronics12051253
Ren YLiu DLiu CXiong QFu JWang L(2023)A Universal Audio Steganalysis Scheme Based on Multiscale Spectrograms and DeepResNetIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.314112120:1(665-679)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TDSC.2022.3141121
Zhuo PYan DYing KWang RDong L(2023)Audio steganography cover enhancement via reinforcement learningSignal, Image and Video Processing10.1007/s11760-023-02819-118:2(1007-1013)Online publication date: 25-Oct-2023
https://doi.org/10.1007/s11760-023-02819-1
Chen LWang RDong LYan D(2023)Imperceptible adversarial audio steganography based on psychoacoustic modelMultimedia Tools and Applications10.1007/s11042-023-14772-982:17(26451-26463)Online publication date: 2-Mar-2023
https://doi.org/10.1007/s11042-023-14772-9
Shehab DAlhaddad M(2022)Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future ResearchSymmetry10.3390/sym1401011714:1(117)Online publication date: 10-Jan-2022
https://doi.org/10.3390/sym14010117
Zhang MLi ZZhang PZhang YLuo X(2022)A Novel High-Capacity Behavioral Steganographic Method Combining Timestamp Modulation and Carrier Selection Based on Social NetworksSymmetry10.3390/sym1401011114:1(111)Online publication date: 8-Jan-2022
https://doi.org/10.3390/sym14010111
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