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IBM SYSTEMS JOURNAL, VOL 35, NOS 3&4, 1996 0018-8670/96/$5.00 1996 IBM BENDER ET AL.
Data hiding, a form of steganography, embeds
data into digital media for the purpose of
identification, annotation, and copyright. Several
constraints affect this process: the quantity of
data to be hidden, the need for invariance of these
data under conditions where a “host” signal is
subject to distortions, e.g., lossy compression,
and the degree to which the data must be immune
to interception, modification, or removal by a third
party. We explore both traditional and novel
techniques for addressing the data-hiding process
and evaluate these techniques in light of three
applications: copyright protection, tamper-
proofing, and augmentation data embedding.
igital representation of media facilitates access
and potentially improves the portability, effi-
ciency, and accuracy of the information presented.
Undesirable effects of facile data access include an
increased opportunity for violation of copyright and
tampering with or modification of content. The moti-
vation for this work includes the provision of protec-
tion of intellectual property rights, an indication of
content manipulation, and a means of annotation.
Data hiding represents a class of processes used to
embed data, such as copyright information, into vari-
ous forms of media such as image, audio, or text with
a minimum amount of perceivable degradation to the
“host” signal; i.e., the embedded data should be invis-
ible and inaudible to a human observer. Note that data
hiding, while similar to compression, is distinct from
encryption. Its goal is not to restrict or regulate access
to the host signal, but rather to ensure that embedded
data remain inviolate and recoverable.
Two important uses of data hiding in digital media are
to provide proof of the copyright, and assurance of
content integrity. Therefore, the data should stay hid-
den in a host signal, even if that signal is subjected to
manipulation as degrading as filtering, resampling,
cropping, or lossy data compression. Other applica-
tions of data hiding, such as the inclusion of augmen-
tation data, need not be invariant to detection or
removal, since these data are there for the benefit of
both the author and the content consumer. Thus, the
techniques used for data hiding vary depending on the
quantity of data being hidden and the required invari-
ance of those data to manipulation. Since no one
method is capable of achieving all these goals, a class
of processes is needed to span the range of possible
applications.
The technical challenges of data hiding are formida-
ble. Any “holes” to fill with data in a host signal,
either statistical or perceptual, are likely targets for
removal by lossy signal compression. The key to suc-
cessful data hiding is the finding of holes that are not
suitable for exploitation by compression algorithms.
A further challenge is to fill these holes with data in a
way that remains invariant to a large class of host sig-
nal transformations.
D
Techniques for data
hiding
by W. Bender
D. Gruhl
N. Morimoto
A. Lu
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