
MPSTEG-COLOR: A NEW STEGANOGRAPHY IN THE PIXEL DOMAIN
Author(s) -
Giacomo Cancelli
Publication year - 2012
Publication title -
journal of the siena academy of sciences
Language(s) - English
Resource type - Journals
eISSN - 2279-882X
pISSN - 2279-8811
DOI - 10.4081/412
Subject(s) - steganography , cover (algebra) , computer science , information hiding , embedding , digital watermarking , set (abstract data type) , watermark , image (mathematics) , artificial intelligence , pattern recognition (psychology) , computer vision , signal (programming language) , pixel , theoretical computer science , mechanical engineering , engineering , programming language
The main goal of any steganographic algorithm is that of hiding a message within an innocuous signal in such a way that the very presence of the hidden message remains secret. The opposite effort of determining the presence of a hidden message within a cover signal is carried out by steganalyzer programs. Blind steganalyzers do not know the steganographic technique used to hide the message, hence they rely on a statistical analysis to understand whether a given signal contains hidden data or not, however this analysis disregards the semantic content of the cover signal. Given the observation above, it may be argued that, from a steganographic point of view it is preferable to embed the watermark at higher semantic levels, e.g. by modifying structural elements of the host signal like lines, edges or flat areas in the case of still images. In this work we presented a new steganographic algorithm for color images (MPSteg-color). It is based on Matching Pursuit (MP) decomposition of the host image. The idea behind this work is to adaptively choose the elements of a redundant basis to represent the host image. In this way, the image is expressed as the composition of a set of structured elements resembling basic image structures such as lines, corners, and flat regions. We argue that embedding the secret message at this, more semantic, level results in a lower modification of the low-level statistical properties of the image, and hence in a lower detectability of the presence of the hidden message. The results confirm the validity of the proposed approach