Intelligent scalable image watermarking robust against progressive DWT-based compression using genetic algorithms

Image watermarking refers to the process of embedding an authentication message, called watermark, into the host image to uniquely identify the ownership. In this paper a novel, intelligent, scalable, robust wavelet-based watermarking approach is proposed. The proposed approach employs a genetic algorithm to find nearly optimal positions to insert watermark. The embedding positions coded as chromosomes and GA operators (e.g. selection, crossover, mutation and elitism), are used to find the nearly optimal embedding positions. A fitness function, which includes both factors related to transparency and robustness, is used to assess and compare chromosomes. The watermarked test images do not show any perceptual degradation. This approach supports scalable watermark detection and provides robustness against progressive wavelet image compression. The experimental results very efficiently prove the robustness of the approach against progressive wavelet image coding even at very low bit-rates and some other attacks. This approach is a good candidate for providing efficient authentication for secure and progressive image transmission applications especially over heterogeneous networks, such as the Internet.