Improving High Embedding Capacity Using Artificial Immune System: A Novel Approach for Data Hiding - Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE-2012) - page 208

positional notation of writing less significant digit further to the right. It is anal-
ogous to the least significant digit of a decimal integer, which is the digit in the
ones (right-most) position. In referencing specific bits within a binary number, it is
common to assign each bit a bit number, ranging from zero upwards to one less
than the number of bits in the number. However, the order used for this assignment
may be in either direction. Both orderings are used (in different contexts), which is
why ''lsb'' is often used to designate the units bit instead of a bit number, which
has the potential for confusion.
Data Hiding by PVD
The pixel-value differencing (PVD) method proposed by Wu and Tsai [ 25 ] can
successfully provide both high embedding capacity and outstanding impercepti-
bility for the stego image. The PVD method segments the cover image into
nonoverlapping blocks containing two connecting pixels and modifies the pixel
difference in each block (pair) for data embedding.
Data Hiding by GLM
In 2004, Potdar et al. [ 3 ] proposes gray level modification (GLM) technique which
is used to map data by modifying the gray level of the image pixels. Gray level
modification steganography is a technique to map data (not embed or hide it) by
modifying the gray level values of the image pixels. GLM technique uses the
concept of odd and even numbers to map data within an image. It is a one-to-one
mapping between the binary data and the selected pixels in an image. From a given
image, a set of pixels is selected based on a mathematical function. The gray level
values of those pixels are examined and compared with the bit stream that is to be
mapped in the image.
Artificial Immune Systems
Artificial immune systems (AIS) are a class of computationally intelligent systems
inspired by the principles and processes of the vertebrate immune system. The
algorithms typically exploit the immune system's characteristics of learning and
memory to solve a problem.
The field of AIS is concerned with abstracting the structure and function of
the immune system to computational systems, and investigating the application of
these systems toward solving computational problems from mathematics, engi-
neering, and information technology. AIS is a subfield of Biologically inspired