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      System steganalysis with automatic fingerprint extraction

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          Abstract

          This paper tries to tackle the modern challenge of practical steganalysis over large data by presenting a novel approach whose aim is to perform with perfect accuracy and in a completely automatic manner. The objective is to detect changes introduced by the steganographic process in those data objects, including signatures related to the tools being used. Our approach achieves this by first extracting reliable regularities by analyzing pairs of modified and unmodified data objects; then, combines these findings by creating general patterns present on data used for training. Finally, we construct a Naive Bayes model that is used to perform classification, and operates on attributes extracted using the aforementioned patterns. This technique has been be applied for different steganographic tools that operate in media files of several types. We are able to replicate or improve on a number or previously published results, but more importantly, we in addition present new steganalytic findings over a number of popular tools that had no previous known attacks.

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          Hierarchical clustering schemes.

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            Intrusion Detection using Naive Bayes Classifier with Feature Reduction

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              Edge Adaptive Image Steganography Based on LSB Matching Revisited

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                Author and article information

                Contributors
                Role: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2018
                25 April 2018
                : 13
                : 4
                : e0195737
                Affiliations
                [1 ] University Carlos III of Madrid, Leganés, Madrid, Spain
                [2 ] University of Kent, Canterbury, United Kingdom
                George Mason University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-5442-953X
                Article
                PONE-D-17-26781
                10.1371/journal.pone.0195737
                5919007
                29694366
                03884cce-f8d2-483b-825e-aa4613303bd6
                © 2018 Cervantes et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 July 2017
                : 28 March 2018
                Page count
                Figures: 2, Tables: 19, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/N024192/1
                Award Recipient :
                This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Grant EP/N024192/1 (J.C.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer and Information Sciences
                Information Technology
                Data Processing
                Social Sciences
                Linguistics
                Grammar
                Physical Sciences
                Physics
                Thermodynamics
                Entropy
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Medicine and Health Sciences
                Diagnostic Medicine
                Clinical Laboratory Sciences
                Forensics
                Social Sciences
                Law and Legal Sciences
                Forensics
                Engineering and Technology
                Signal Processing
                Statistical Signal Processing
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Social Sciences
                Economics
                Labor Economics
                Employment
                Custom metadata
                All data files are available from the UC3M-UKENT Stegoanalysis data archive (accession number doi: 0.21950/7SYAAO). Access page: https://edatos.consorciomadrono.es/dataset.xhtml?persistentId=doi%3A10.21950%2F7SYAAO Software is available from Github: https://github.com/acervs/systemSteganalysis.

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