The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Watermark Embedding for Multiscale Error Diffused Halftone Images by Adopting Visual Cryptography
|
Author(s): Yuanfang Guo (HKUST, Hong Kong SAR, China), Oscar C. Au (HKUST, Hong Kong SAR, China)and Ketan Tang (HKUST, Hong Kong SAR, China)
Copyright: 2017
Pages: 20
Source title:
Biometrics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0983-7.ch007
Purchase
|
Abstract
Error Diffusion has been widely adopted in the printing industry due to its good visual quality and simple implementation. However, error diffusion still possesses its own deficiencies. Thus multiscale error diffusion (MED) has been developed, and this method outperforms traditional error diffusion according to extensive research results. The majority of previous halftone image watermarking techniques cannot be directly applied to MED halftone images. Since there is no halftone visual watermarking (HVW) method for MED halftone images in existing methods, we propose the first HVW method for MED halftone images, Copyright Protecting Multiscale Error Diffusion (CoP-MED), in this paper. By adopting the visual cryptography principle, CoP-MED can effectively embed a secret pattern into two MED halftone images, where the secret pattern can be decoded clearly by simply overlaying the two stego halftone images or performing not-exclusive-or operation between them. Parameter selection is also discussed based on the experimental results. Later, in comparison tests, CoP-MED shows superior performance compared to existing works.
Related Content
Ajay Rawat, Shivani Gambhir.
© 2017.
19 pages.
|
Abhijit Chandra, Srideep Maity.
© 2017.
15 pages.
|
Swanirbhar Majumder, Saurabh Pal.
© 2017.
26 pages.
|
Fouad Farouk Jabri.
© 2017.
32 pages.
|
Francisco Pacheco Andrade, Teresa Coelho Moreira.
© 2017.
13 pages.
|
Swanirbhar Majumder, Smita Majumder.
© 2017.
31 pages.
|
Yuanfang Guo, Oscar C. Au, Ketan Tang.
© 2017.
20 pages.
|
|
|