This project explains about how data-hiding system designed for the binary images to the morphological transform field works for substantiation objective. In order to attain blind watermark extraction it is quite intricate to make use of the minute coefficients like an actually location map for discovering the data-hiding areas.

So from this particular observation all of us can propose that there is binary wavelet transform for the purpose to trail the slight edges that aids in blind watermark extraction along with cryptographic signature. The block size of the image should be 2*2 pixels instead of 3*3 pixel blocks. For that reason we are able to track the minor edges and gains lower computational involvedness. A new efficient Backward-Forward Minimization process is projected that reflects on both proper backwardly those adjacent progressed embedded persons as well as forwardly that unprocessed flip able candidates which may have an effect on by turning over the existing pixel.

This project is done for determining the data-hiding locations for binary images. The data embedding is generally known as orthogonal embedding and we work on the images of 2 pixel blocks and merge the two processing which run simultaneously. By the orthogonal embedding of the binary images the entire visual deformation can be reduced to a great extent. The results of experiments may exhibit the argument in best possible manner.

With the intention of attaining blind watermark extraction it is to a certain extent intricate to take advantage of the little coefficients like a location map to discover the data-hiding areas. Therefore from this observation we can put forward that there is binary wavelet transform to trace the minor edges that aids in blind watermark extraction along with cryptographic signature. The block size of the image should be 2*2 pixels instead of 3*3 pixel blocks. In view of that we are able to track the minor edges and gains lower computational convolution.

Download  Orthogonal Data Embedding for Binary Images in Morphological Transform Domain A High Capacity Approach.