Blind Wavelet-Based Image Watermarking

*Abeer D. Algarni and Hanaa A. Abdallah*

### **Abstract**

In this chapter, the watermarking technique is blind; blind watermarking does not need any of the original images or any information about it to recover watermark. In this technique the watermark is inserted into the high frequencies. Threelevel wavelet transform is applied to the image, and the size of the watermark is equal to the size of the detailed sub-band. Significant coefficients are used to embed the watermark. The proposed technique depends on quantization. The proposed watermarking technique generates images with less degradation.

**Keywords:** watermarking, discrete wavelet transform, quantization, blind, coefficients, peak signal-to-noise ratio, normalization, correlation

#### **1. Introduction**

Watermarking methods operating in the wavelet domain have become attractive because they have inherent robustness against compression if the low-frequency band is selected for watermark embedding, and, additionally, the wavelet transform provides a multiresolution representation of images, which can be exploited to build more efficient watermark detection schemes. The history of watermarking is presented here. Zhu et al. [1] proposed adding a mark, a Gaussian sequence of pseudorandom real numbers, into all the high-pass bands in the wavelet domain. An algorithm developed by Xia et al. [2] utilizes large DWT coefficients of the highand mid-frequency bands to embed a random Gaussian distributed watermark sequence. Dugad et al. [3] provided a method to embed a Gaussian sequence of pseudorandom real numbers into selected coefficients in all detailed sub-bands with magnitude above a given threshold in a three-level decomposition with Daubechies-8 filters. In general, the watermark embedded in low-pass bands of the wavelet domain is robust to a group of attacks such as low-pass filtering, Gaussian noise, and lossy compression but affects the fidelity of the watermarked image and that in high-pass bands is resistant to another set of attacks such as histogram equalization, intensity adjustment, and gamma correction [4].
