Automatic visible watermarking of images

A. Ravishankar Rao; Gordon W. Braudaway; Frederick C. Mintzer

doi:10.1117/12.304677

1 April 1998 Automatic visible watermarking of images

A. Ravishankar Rao, Gordon W. Braudaway, Frederick C. Mintzer

Proceedings Volume 3314, Optical Security and Counterfeit Deterrence Techniques II; (1998) https://doi.org/10.1117/12.304677
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

Abstract

Visible image watermarking has become an important and widely used technique to identify ownership and protect copyrights to images. A visible image watermark immediately identifies the owner of an image, and if properly constructed, can deter subsequent unscrupulous use of the image. The insertion of a visible watermark should satisfy two conflicting conditions: the intensity of the watermark should be strong enough to be perceptible, yet it should be light enough to be unobtrusive and not mar the beauty of the original image. Typically such an adjustment is made manually, and human intervention is required to set the intensity of the watermark at the right level. This is fine for a few images, but is unsuitable for a large collection of images. Thus, it is desirable to have a technique to automatically adjust the intensity of the watermark based on some underlying property of each image. This will allow a large number of images to be automatically watermarked, this increasing the throughput of the watermarking stage. In this paper we show that the measurement of image texture can be successfully used to automate the adjustment of watermark intensity. A linear regression model is used to predict subjective assessments of correct watermark intensity based on image texture measurements.

Citation Download Citation

A. Ravishankar Rao, Gordon W. Braudaway, and Frederick C. Mintzer "Automatic visible watermarking of images", Proc. SPIE 3314, Optical Security and Counterfeit Deterrence Techniques II, (1 April 1998); https://doi.org/10.1117/12.304677

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available