Abstract
This Chapter presents a brief review of the work done during 1990-2013, in the application of intelligent techniques to digital image watermarking. The review discusses many papers of the gray-scale and color images than other multimedia. The review is structured by considering the type of technique applied to solve the problem as an important dimension. Consequently the papers are grouped into the following two families, (i) Neural networks, (ii) Fuzzy logic. Comparative analysis of different techniques is also presented. Finally, the review is concluded with future directions.
TopBackground
Fuzzy set theory, given by Zadeh (1965), has found a number of applications. Fuzzy logic is a problem-solving control methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It uses an imprecise but very descriptive language to deal with input data more like a human operator and it has a variety of applications (Zimmermann, 1996). Fuzzy logic can also be used to derive fuzzy 'if-then' rules from data to solve classification problems.
An artificial neural network (ANN) (Lacher, Coats, Sharma, & Fantc, 1995) is an information-processing paradigm inspired by the way biological nervous system, such as the brain, processes information. A neural network is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing. Due to the advantages like adaptive learning, self-organization, real time operation and fault tolerance via redundant information coding, neural networks found extensive applications in digital image watermarking. The MLP (Rumelhart, Hinton, & Williams, 1986), RBFNN (Moody & Darken, 1989), Recurrent Neural Network (Hopfield, 1982) are some of the popular neural network architectures. They differ in aspects including the type of learning, node connection mechanism, the training algorithm etc. Back propagation neural network (BPNN) is a supervised learning technique used by MLP for training the network (Rosenblatt & Frank, 1961).
Key Terms in this Chapter
Radial Basis Function Neural Network: This is an artificial neural network which uses radial basis functions as activation functions.
Back Propagation Neural Network: This is a supervised learning technique used by MLP for training the network
Neural Networks: It is an art of imitating human brain activities through artificial intelligence.
Fuzzy Logic: It is an art of approximation technique to simulate human brain activities.
Digital Image Watermarking: This is defined as inserting authentication information into digital images.
Artificial Intelligence: This is computer science technology that studies and develops intelligent machines and software.
Digital Rights Management: The term refers to the protection of copyrights of digital media files, which can be implemented through encryption and decryption, steganography or digital watermarking