PROJECT TITLE :

Features Classification Forest: A Novel Development That Is Adaptable To Robust Blind Watermarking Techniques - 2017

ABSTRACT:

A completely unique watermarking theme is proposed that could substantially improve current watermarking techniques. This theme exploits the options of micro pictures of watermarks to create association rules and embeds the foundations into a bunch image rather than the bit stream of the watermark, which is usually employed in digital watermarking. Next, similar micro images with the same rules are collected or even created from the host image to simulate an extracted watermark. This methodology, known as the features classification forest, will achieve blind extraction and is adaptable to any watermarking theme employing a quantization-primarily based mechanism. Furthermore, a larger size watermark can be accepted while not an adverse impact on the imperceptibility of the host image. The experiments demonstrate the successful simulation of watermarks and the applying to 5 totally different watermarking schemes. One among them is slightly adjusted from a reference to particularly resist JPEG compression, and also the others show native blessings to resist totally different image processing attacks.