A high throughput system for intelligent watermarking of bi-tonal images
In intelligent watermarking of document images, evolutionary computing (EC) techniques are employed in order to determine embedding parameters of digital watermarking systems such that the trade-off between watermark robustness and image fidelity is optimized. However, existing techniques for intelligent watermarking rely on full optimization of embedding parameters for each image. This approach does not apply to high data rate applications due to its high computational complexity. In this paper, a novel intelligent watermarking technique based on Dynamic Particle Swarm Optimization (DPSO) is proposed. Intelligent watermarking of bi-tonal image streams is formulated as a dynamic optimization problem. This population-based technique allows to evolve a diversified set of solutions (i.e., embedding parameters) to an optimization problem, and solutions from previous optimizations are archived and re-considered prior to triggering new optimizations. In such case, costly optimization may be replaced by direct recall of quasi identical solutions. Simulations involving the intelligent watermarking of several long streams of homogeneous PDF document images resulted in a decrease of computational burden (number of fitness evaluations) of up to 97.2% with a negligible impact on accuracy.
intelligent watermarking evolutionary computing digital watermarking particle swarm optimization dynamic optimization