Biomedical Engineering Reference
In-Depth Information
rate, or high resistance to modification, but not both. As one increases, the other
must decrease. While this can be shown mathematically for some data-hiding
systems such as a spread spectrum, it seems to hold true for all data-hiding sys-
tems. In any system, you can trade bandwidth for robustness by exploiting
redundancy. The quantity of embedded data and the degree of host signal modi-
fication vary from application to application. Consequently, different techniques
are employed for different applications. Several prospective applications of data
hiding. An application that requires a minimal amount of embedded data is the
placement of digital watermark. The embedded data are used to place an indication
of ownership in the host signal, serving the same purpose as an author's signature
or a company logo. A second application for data hiding is tamper proofing. It is
used to indicate that the host signal has been modified from its authored state.
Modification to the embedded data indicates that the host signal has been changed
in some way. A third application, feature location, requires more data to be
embedded. In this application, the embedded data are hidden in specific locations
within an image. It enables one to identify individual content features, e.g., the
name of the person on the left versus the right side of an image. Typically, feature
location data are not subject to intentional removal. However, it is expected that
the host signal might be subjected to a certain degree of modification, e.g., images
are routinely modified by scaling, cropping, and tone scale enhancement. As a
result, feature location data-hiding techniques must be immune to geometric and
nongeometric modifications of a host signal.
Data Hiding Various Streams
Data Hiding in Still Images
Data hiding in still images [
11
] presents a variety of challenges that arise due to the
way the human visual system (HVS) works and the typical modifications that
images undergo. Additionally, still images provide a relatively small host signal in
which to hide data. A fairly typical 8-bit picture of 200 9 200 pixels provides
approximately 40 kilobytes (kb) of data space in which to work. This is equivalent
to only around 5 s of telephone-quality audio or less than a single frame of NTSC
television. Also, it is reasonable to expect that still images will be subject to
operations ranging from simple affine transforms to nonlinear transforms such as
cropping, blurring, filtering, and lossy compression. Practical data-hiding tech-
niques need to be resistant to as many of these transformations as possible. Despite
these challenges, still images are likely candidates for data hiding. There are many
attributes of the HVS that are potential candidates for exploitation in a data-hiding
system, including our varying sensitivity to contrast as a function of spatial fre-
quency and the masking effect of edges (both in luminance and The HVS has low
sensitivity to small changes in luminance, being able to perceive changes of no less
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