ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Stochastic Approach to Content Adaptive Digital Image Watermarking
IH '99 Proceedings of the Third International Workshop on Information Hiding
Second Generation Benchmarking and Application Oriented Evaluation
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Introducing a watermarking with a multi-objective genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Geometric Attacks on Image Watermarking Systems
IEEE MultiMedia
A hybrid watermarking technique applied to digital images
Applied Soft Computing
Methods for image authentication: a survey
Multimedia Tools and Applications
A novel watermarking scheme based on PSO algorithm
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Intelligent Watermarking with Multi-objective Population Based Incremental Learning
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
A high throughput system for intelligent watermarking of bi-tonal images
Applied Soft Computing
Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering
IEEE Transactions on Neural Networks
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Population-based evolutionary computation (EC) is widely used to optimize embedding parameters in intelligent watermarking systems. Candidate solutions generated with these techniques allow finding optimal embedding parameters of all blocks of a cover image. However, using EC techniques for full optimization of a stream of high-resolution grayscale face images is very costly. In this paper, a blockwise multi-resolution clustering (BMRC) framework is proposed to reduce this cost. During training phase, solutions obtained from multi-objective optimization of reference face images are stored in an associative memory. During generalization operations, embedding parameters of an input image are determined by searching for previously stored solutions of similar sub-problems in memory, thereby eliminating the need for full optimization for the whole face image. Solutions for sub-problems correspond to the most common embedding parameters for a cluster of similar blocks in the texture feature space. BMRC identifies candidate block clusters used for embedding watermark bits using the robustness score metric. It measures the texture complexity of image block clusters and can thereby handle watermarks of different lengths. The proposed framework implements a multi-hypothesis approach by storing the optimization solutions according to different clustering resolutions and selecting the optimal resolution at the end of the watermarking process. Experimental results on the PUT face image database show a significant reduction in complexity up to 95.5 % reduction in fitness evaluations compared with reference methods for a stream of 198 face images.