Cryptography Reference
In-Depth Information
contains only something else. A variety of vessels are possible, such as digital
images, sound clips, and even executable files. In recent years, many stegano-
graphic programs have been posted on Internet Web pages. Most of them use
image data for the container of the secret information. Some of them use the
least significant bits of the image data to hide secret data. Other programs
embed the secret information in a specific band of the spatial frequency com-
ponent of the carrier. Some other programs make use of the sampling error in
image digitization. However, all those steganographic techniques are limited in
terms of information hiding capacity. They can embed only 5−15% of the ves-
sel image at the best. Therefore, traditional steganography is more oriented to
watermarking of computer data than to secret person-person communication
applications.
We have invented a new technique to hide secret information in an image.
This is not based on a programming technique, but is based on the property of
human vision system. Its information hiding capacity can be as large as 50% of
the original image data. This could open new applications for steganography
leading to a more secure Internet communication age.
Digital images are categorized as either binary (black-and-white) or multi-
valued pictures despite their actual color. We can decompose an n-bit image
into a set of n binary images by bit-slicing operations [1, 2]. Therefore, binary
image analysis is essential to all digital image processing. Bit slicing is not
necessarily the best in the standard binary coding system (We call it Pure-
Binary Coding system (PBC)), but in some cases the Canonical Gray Coding
system (CGC) is much better [3].
8.1.1 The Complexity of Binary Images
The method of steganography outlined in this chapter makes use of the com-
plex regions of an image to embed data. There is no standard definition of
image complexity. Kawaguchi discussed this problem in connection with the
image thresholding problem, and proposed three types of complexity mea-
sures [4, 5, 6]. In this chapter we adopted a black-and-white border image
complexity.
The Definition of Image Complexity
The length of the black-and-white border in a binary image is a good measure
for image complexity. If the border is long, the image is complex, otherwise
it is simple. The total length of the black-and-white border equals to the
summation of the number of color-changes along the rows and columns in
an image. For example, a single black pixel surrounded by white background
pixels has the boarder length of 4.
We will define the image complexity α for an mm size binary image by
the following.
