Steganalysis of watermarking techniques using image quality metrics

I. Avcibaş, N. Memon, B. Sankur

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.

    Original languageEnglish (US)
    Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
    EditorsP.W. Wong, E.J. Delp
    Pages523-531
    Number of pages9
    Volume4314
    DOIs
    StatePublished - 2001
    EventSecurity and Watermarking of Multimedia Contents III - San Jose, CA, United States
    Duration: Jan 22 2001Jan 25 2001

    Other

    OtherSecurity and Watermarking of Multimedia Contents III
    CountryUnited States
    CitySan Jose, CA
    Period1/22/011/25/01

    Fingerprint

    Watermarking
    Image quality
    Regression analysis
    Analysis of variance (ANOVA)
    Classifiers
    regression analysis
    analysis of variance
    blurring
    classifiers
    leaves

    Keywords

    • Image quality measures
    • Multivariate regression analysis
    • Steganalysis
    • Watermarking

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Condensed Matter Physics

    Cite this

    Avcibaş, I., Memon, N., & Sankur, B. (2001). Steganalysis of watermarking techniques using image quality metrics. In P. W. Wong, & E. J. Delp (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 4314, pp. 523-531) https://doi.org/10.1117/12.435436

    Steganalysis of watermarking techniques using image quality metrics. / Avcibaş, I.; Memon, N.; Sankur, B.

    Proceedings of SPIE - The International Society for Optical Engineering. ed. / P.W. Wong; E.J. Delp. Vol. 4314 2001. p. 523-531.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Avcibaş, I, Memon, N & Sankur, B 2001, Steganalysis of watermarking techniques using image quality metrics. in PW Wong & EJ Delp (eds), Proceedings of SPIE - The International Society for Optical Engineering. vol. 4314, pp. 523-531, Security and Watermarking of Multimedia Contents III, San Jose, CA, United States, 1/22/01. https://doi.org/10.1117/12.435436
    Avcibaş I, Memon N, Sankur B. Steganalysis of watermarking techniques using image quality metrics. In Wong PW, Delp EJ, editors, Proceedings of SPIE - The International Society for Optical Engineering. Vol. 4314. 2001. p. 523-531 https://doi.org/10.1117/12.435436
    Avcibaş, I. ; Memon, N. ; Sankur, B. / Steganalysis of watermarking techniques using image quality metrics. Proceedings of SPIE - The International Society for Optical Engineering. editor / P.W. Wong ; E.J. Delp. Vol. 4314 2001. pp. 523-531
    @inproceedings{deae0ee2be724966a7b65d26b9a71536,
    title = "Steganalysis of watermarking techniques using image quality metrics",
    abstract = "In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.",
    keywords = "Image quality measures, Multivariate regression analysis, Steganalysis, Watermarking",
    author = "I. Avcibaş and N. Memon and B. Sankur",
    year = "2001",
    doi = "10.1117/12.435436",
    language = "English (US)",
    volume = "4314",
    pages = "523--531",
    editor = "P.W. Wong and E.J. Delp",
    booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

    }

    TY - GEN

    T1 - Steganalysis of watermarking techniques using image quality metrics

    AU - Avcibaş, I.

    AU - Memon, N.

    AU - Sankur, B.

    PY - 2001

    Y1 - 2001

    N2 - In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.

    AB - In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.

    KW - Image quality measures

    KW - Multivariate regression analysis

    KW - Steganalysis

    KW - Watermarking

    UR - http://www.scopus.com/inward/record.url?scp=0034775171&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=0034775171&partnerID=8YFLogxK

    U2 - 10.1117/12.435436

    DO - 10.1117/12.435436

    M3 - Conference contribution

    VL - 4314

    SP - 523

    EP - 531

    BT - Proceedings of SPIE - The International Society for Optical Engineering

    A2 - Wong, P.W.

    A2 - Delp, E.J.

    ER -