A Novel Quantization Watermarking Based On Normalized Correlation

With the rapid development of computer network technology and multimedia technology, digital watermarking technology was thought as the effective method for intellectual property protection and digital multimedia anti-counterfeiting and has become a focus of research in the field of information security. Quantization based watermarking are proposed as one of an important branch of digital watermarking algorithms. And its main idea is to hide dada by changing the signal sampling quantitative values. Particularly, they have the characteristics of high capacity and good imperceptibility, etc.This paper summarizes the existing literature in the digital watermarking algorithm, especially about the representative algorithms of the quantization based watermarking. In addition, it analyzes and discusses the basic principle of the various kinds of algorithm and the characteristics of the watermarking algorithm. And then makes a systematical summarization about the theoretical knowledge of the quantized watermarking.Based on the above analysis, this article presents a novel quantization based watermarking scheme. Watermark embedding is performed through modifying a feature signal generated from the host signal. The feature signal is suggested to choose the normalized correlation between the host vector and a random vector. Information modulation is carried out on the selected feature signal using dither modulation. The watermarked signal is produced to provide the modulated feature in the sense of minimizing the embedding distortion. Meanwhile, we derive the expressions of the embedding distortion and the minimal channel distortion to remove the hidden message. According to them, the watermarking performance can be further improved. The proposed scheme is theoretically invariant to valumetric scaling and can resist stronger noise than the well-known spread transform dither modulation.In addition to the theoretical analysis, numerical simulations on real images also show that it achieves the good imperceptibility and strong robustness against a wide range of attacks, such as different strengths of additive white gaussian noise (AWGN) attacks, valumetric scaling, non-linear distortions and so on.