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A Study on Machine Learning for Steganalysis

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Ki-Hyun JungAuthors Info & Claims

ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing

Pages 12 - 15

https://doi.org/10.1145/3310986.3311000

Published: 25 January 2019 Publication History

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Abstract

Data security is very important when sensitive data are transmitted over the Internet. Steganography and steganalysis techniques can solve the problem of copyright, ownership, and detection malicious data. Steganography is to hide secret data without distortion and steganalysis is to detect the presence of hidden data. In this paper, steganography and steganalysis techniques are described together with machine learning frameworks to show that machine learning framework can be used to detect the secret data hiding in image using steganography algorithms.

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Cited By

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Mohan AAmritha PSethumadhavan M(2024)Machine Learning Based Detection of Hidden Data in Network PacketsInformation Systems for Intelligent Systems10.1007/978-981-99-8612-5_44(543-552)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-99-8612-5_44
Shah HOza PAgrawal S(2023)Data Encryption Approach Using Hybrid Cryptography and Steganography with Combination of Block CiphersAdvancements in Smart Computing and Information Security10.1007/978-3-031-23095-0_4(59-69)Online publication date: 11-Jan-2023
https://doi.org/10.1007/978-3-031-23095-0_4
Tasevski IDobreva JAndonov SMihajloska HPopovska-Mitrovikj ADimitrova V(2023)StegIm: Image in Image SteganographyICT Innovations 2022. Reshaping the Future Towards a New Normal10.1007/978-3-031-22792-9_2(13-25)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-22792-9_2
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Index Terms

A Study on Machine Learning for Steganalysis
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems
      1. Artificial immune systems

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Published In

ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing

January 2019

268 pages

ISBN:9781450366120

DOI:10.1145/3310986

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 January 2019

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ICMLSC 2019

ICMLSC 2019: 2019 the 3rd International Conference on Machine Learning and Soft Computing

January 25 - 28, 2019

Da Lat, Viet Nam

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Cited By

View all

Mohan AAmritha PSethumadhavan M(2024)Machine Learning Based Detection of Hidden Data in Network PacketsInformation Systems for Intelligent Systems10.1007/978-981-99-8612-5_44(543-552)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-99-8612-5_44
Shah HOza PAgrawal S(2023)Data Encryption Approach Using Hybrid Cryptography and Steganography with Combination of Block CiphersAdvancements in Smart Computing and Information Security10.1007/978-3-031-23095-0_4(59-69)Online publication date: 11-Jan-2023
https://doi.org/10.1007/978-3-031-23095-0_4
Tasevski IDobreva JAndonov SMihajloska HPopovska-Mitrovikj ADimitrova V(2023)StegIm: Image in Image SteganographyICT Innovations 2022. Reshaping the Future Towards a New Normal10.1007/978-3-031-22792-9_2(13-25)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-22792-9_2
Bansal MRatan R(2022)Comprising Survey of Steganography & Cryptography: Evaluations, Techniques and Trends in Future Research2022 8th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC56524.2022.10009533(315-323)Online publication date: 1-Dec-2022
https://doi.org/10.1109/ICSC56524.2022.10009533
Shu YQin RHe YLi YJiang RWu Z(2022)Deep Residual Neural Networks with Attention Mechanism for Spatial Image Steganalysis2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00260(1721-1727)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00260
Chhikara SKumar R(2022)Information theoretic steganalysis of processed image LSB steganographyMultimedia Tools and Applications10.1007/s11042-022-13931-882:9(13595-13615)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13931-8
Sivagurunathan PArchana MDurga Shree NYuvashrie G(2022)Image Steganography for Confidential Communication and Secured Data StoringSoft Computing for Security Applications10.1007/978-981-19-3590-9_24(315-324)Online publication date: 30-Sep-2022
https://doi.org/10.1007/978-981-19-3590-9_24
Durafe APatidar V(2022)Securing the COVID Patients’ Medical Records Using Encrypted Image SteganographyICT Systems and Sustainability10.1007/978-981-16-5987-4_43(421-440)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-981-16-5987-4_43
Tasevski INikolovska VPetrova ADobreva JPopovska-Mitrovikj ADimitrova V(2022)StegYou: Model for Hiding, Retrieving and Detecting Digital Data in ImagesProceedings of the Future Technologies Conference (FTC) 2022, Volume 210.1007/978-3-031-18458-1_32(467-485)Online publication date: 13-Oct-2022
https://doi.org/10.1007/978-3-031-18458-1_32
Gupta APramanik SBui HIbenu N(2021)Machine Learning and Deep Learning in Steganography and SteganalysisMultidisciplinary Approach to Modern Digital Steganography10.4018/978-1-7998-7160-6.ch004(75-98)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7160-6.ch004
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