IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_100.html
   My bibliography  Save this book chapter

A Collaborative Method for Minimizing Tampering of Image with Commuted Concept of Frazile Watermarking

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Abhishek Kumar

    (Chitkara University, Assistant Professor Department of Computer Science & Engineering Chitkara University Institute of Engineering and Technology)

  • Jyotir Moy Chatterjee

    (Lord Buddha Education Foundation, Department of IT)

  • Avishek Choudhuri

    (Amity University Patna, Department of IT)

  • Pramod Singh Rathore

    (Chitkara University, Assistant Professor Department of Computer Science & Engineering Chitkara University Institute of Engineering and Technology)

Abstract

The study has proposed a hybrid watermarking method. It consists of numerous leveled thresholds, in which pixel-inferred and pixel determined watermark information are conveyed through the slightest significant bits of all pixels. It basically masks specific location for buying that region watermarked, and additionally may be applicable for content retrieval. The content may be watermarked without intermixing the records pixel and provide a green result. The experimented end result is commonly based totally on dynamic threshold. Considering assumptions, it is proposed that data pixel is absolute impartial from every different in order that if threshold activated over a probe picture dynamically, the records pixel tends no longer to combine collectively and attain in efficient way. The paper consists of different theory blended together to carry out. This is greater like content based watermarked technique.

Suggested Citation

  • Abhishek Kumar & Jyotir Moy Chatterjee & Avishek Choudhuri & Pramod Singh Rathore, 2020. "A Collaborative Method for Minimizing Tampering of Image with Commuted Concept of Frazile Watermarking," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 985-994, Springer.
  • Handle: RePEc:spr:sprchp:978-3-030-41862-5_100
    DOI: 10.1007/978-3-030-41862-5_100
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-41862-5_100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.