This paper focuses on steganography based on pixel intensity value decomposition. A number of existing schemes such
as binary, Fibonacci, Prime, Natural, Lucas, and Catalan-Fibonacci (CF) are evaluated in terms of payload capacity and
stego quality. A new technique based on a specific representation is proposed to decompose pixel intensity values into
16 (virtual) bit-planes suitable for embedding purposes. The proposed decomposition has a desirable property whereby
the sum of all bit-planes does not exceed the maximum pixel intensity value, i.e. 255. Experimental results demonstrate
that the proposed technique offers an effective compromise between payload capacity and stego quality of existing
embedding techniques based on pixel intensity value decomposition. Its capacity is equal to that of binary and Lucas,
while it offers a higher capacity than Fibonacci, Prime, Natural, and CF when the secret bits are embedded in 1st Least
Significant Bit (LSB). When the secret bits are embedded in higher bit-planes, i.e., 2nd LSB to 8th Most Significant Bit
(MSB), the proposed scheme has more capacity than Natural numbers based embedding. However, from the 6th bit-plane
onwards, the proposed scheme offers better stego quality. In general, the proposed decomposition scheme has less effect
in terms of quality on pixel value when compared to most existing pixel intensity value decomposition techniques when
embedding messages in higher bit-planes.
One of the common types of steganography is to conceal an image as a secret message in another image which normally called a cover image; the resulting image is called a stego image. The aim of this paper is to investigate the effect of using different cover image quality, and also analyse the use of different bit-plane in term of robustness against well-known active attacks such as gamma, statistical filters, and linear spatial filters. The secret messages are embedded in higher bit-plane, i.e. in other than Least Significant Bit (LSB), in order to resist active attacks. The embedding process is performed in three major steps: First, the embedding algorithm is selectively identifying useful areas (blocks) for embedding based on its lighting condition. Second, is to nominate the most useful blocks for embedding based on their entropy and average. Third, is to select the right bit-plane for embedding. This kind of block selection made the embedding process scatters the secret message(s) randomly around the cover image. Different tests have been performed for selecting a proper block size and this is related to the nature of the used cover image. Our proposed method suggests a suitable embedding bit-plane as well as the right blocks for the embedding. Experimental results demonstrate that different image quality used for the cover images will have an effect when the stego image is attacked by different active attacks. Although the secret messages are embedded in higher bit-plane, but they cannot be recognised visually within the stegos image.
Performance indicators characterizing modern steganographic techniques include capacity (i.e. the quantity
of data that can be hidden in the cover medium), stego quality (i.e. artifacts visibility), security (i.e.
undetectability), and strength or robustness (intended as the resistance against active attacks aimed to
destroy the secret message). Fibonacci based embedding techniques have been researched and proposed in
the literature to achieve efficient steganography in terms of capacity with respect to stego quality. In this
paper, we investigated an innovative idea that extends Fibonacci-like steganography by bit-plane(s)
mapping instead of bit-plane(s) replacement. Our proposed algorithm increases embedding capacity using
bit-plane mapping to embed two bits of the secret message in three bits of a pixel of the cover, at the
expense of a marginal loss in stego quality. While existing Fibonacci embedding algorithms do not use
certain intensities of the cover for embedding due to the limitation imposed by the Zeckendorf theorem,
our proposal solve this problem and make all intensity values candidates for embedding. Experimental
results demonstrate that the proposed technique double the embedding capacity when compared to existing
Fibonacci methods, and it is secure against statistical attacks such as RS, POV, and difference image
histogram (DIH).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.