International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Fuzzy Logic based watermarking using non – blind HVS technique M Suneetha1, K Meenakshi2, 1Assistant 2Assistant
Professor, Department of ECE, GRIET, Hyderabad, India Professor, Department of ECE, GRIET, Hyderabad, India
---------------------------------------------------------------------***--------------------------------------------------------------------the overlapping effect between luminous adaption and Abstract - This article proposes a non-blind water-marking based on Human Visual System (HVS) and Fuzzy Inference spatial contrast masking. But the drawback of the System (FIS) with Discrete Cosine transform (DCT). The image scheme is it does not consider contrast sensitivity transformed is segmented into 8 × 8 non over-lapping blocks function.A brief introduction of fuzzy logic is provided and for each block parameters such as contrast sensitivity, in section II. Proposed watermark embeding and luminous sensitivity, edge sensitivity, number of on pixels and extraction algorithms are presented in section III. number of regions are designed and are fed to FIS. The output of fuzzy system is a single value which gives a continuous value Simulation results are presented in section IV. of analogous to each DCT coincident. A binary watermark is Conclusions are provided is in section V set in the original image based on perpetual weight premeditated through FIS. The robustness of proposed method is examined by subjecting watermarked image to several attacks. It is institute that fuzzy based watermarking achieves high imperceptibility and robustness to attacks. Key Words: HVS, FIS, DCT
1.INTRODUCTION With the increase in the availability of digital data over internet, the images so distributed can be copied repeatedly with or without error, putting the rights of owner at risk. One way to arrest illegal duplication is to in-sert owner’s identity known as watermark into potentially vulnerable images so that watermarks are inseparable from the images itself. The important requirements of watermarking [1] are centered on three main issues -These are imperceptible quality of watermarked and attacked images, robustness and embedding capacity. Among these the former two parameters are important and these are found to be mutually conflicting. Because of this water-marking may be perceived as an optimization problem to obtain robust embedding and extraction scheme. This paper addresses the problem by constructing fuzzy perpetual masks. Human Visual System is the ultimate receiver of most processed images. Incorporating. Just Noticeable Difference (JND) provides a means to model perpetual redundancy that is the maximum distortion which cannot be perceived by human eye. In [6]-[8] JND thresholds are computed based on background luminance adaption and spatial contrast masking. In [2] a more accurate JND map is deduced considering © 2017, IRJET
|
Impact Factor value: 5.181
|
2. FUZZY LOGIC Fuzzy logic provides a means for incorporating human knowledge in the solution of problems whose formulation is based on vague concepts [1]. FIS is used to map a given input to output based on fuzzy logic. Unlike crisp sets, fuzzy sets are having infinite valued membership functions. The mapping then provides a framework for interpreting the data from which decisions can be made. Principles of Fuzzy set theory: Let Z is a set of elements with element of Z denoted by z. This set is called Universe of Discourse. A fuzzy set A in Z is denoted by µA(Z) that associates each element with an interval between [0, 1].Therefore a fuzzy set is an ordered pair consists of values of z and a membership function that assigns a grade of membership to each z. The FIS has the following blocks as given in Ref. [2]. • Fuzzification: for each scalar input, find the corresponding fuzzy values by mapping the input to the interval [0, 1], using applicable rules defined in rule editor. • Apply an implication method: It is way to use inputs and the knowledge base represented by ifthen rules to creates the output of system. This process is known as implication or inference.
ISO 9001:2008 Certified Journal
|
Page 233