**4. Wavelet-based methods**

The wavelet transform finds a great popularity in the field of watermarking as it is able to decompose the available images into sub-bands, in which watermarks can be embedded [3, 9]. Taking the cue from the spread spectrum method, we embed the data in transform coefficients chosen in a random order. For extraction of the hidden data, the random sequence must be made available to the extractor. Cox et al. [5] were the first to apply the spread spectrum method to data hiding. Transforms such as the DCT and DWT have been used. The use of the DWT has advantages of speed and robustness against wavelet-based compression. Previously, Dugad's algorithm introduced an additive watermarking technique in the wavelet domain [3]. The proposed technique in this paper uses three-level wavelet

transform using Daubechies filter; the watermark is embedded in the highfrequency domain [9], and it is blind algorithm, and the watermark is detected without using the original image. Also this technique uses only the high value coefficients to insert the watermark. Large wavelet coefficients are referred to edges within an image. So, any degradation in this region won't be noticed by the human viewer. Also it is difficult to remove the watermark without distorting the marked image according to the perceptually significant large magnitude wavelet coefficients. Since watermark verification typically consists of a correlation estimation step, which is extremely sensitive to the relative order in which the watermark coefficients are placed within the image, such changes in the location of the watermarked coefficients were unacceptable. Dugad et al. have proposed a spread spectrum method for digital image watermarking in the wavelet domain, which does not require the original image for watermark detection [3]. This method is based on adding the watermark in selected coefficients with significant image energy in the transform domain in order to ensure non-erasability of the watermark. This method has an advantage over the previous methods, which did not use the original in the detection process and could not selectively add the watermark to the significant coefficients, since the locations of such selected coefficients can change due to image manipulations.

characteristics. Any small modifications are performed to improve HVS model. This technique is non-blind as the host image is needed in the watermark extraction.

Dugad et al. [3] presented an additive watermarking method operating in the wavelet domain. This method allowed the detection of the watermark without

1. From all wavelet coefficients (except the low-pass coefficients in LL band and high-pass coefficients in HH band), the coefficients of magnitude higher than t1 are chosen. This proves that only significant coefficients are used. The wavelet coefficients of magnitude higher than t1 depend upon the smoothness

2. Then the zero mean and unit variance watermark are generated with a known seed value; the watermark should be equal in size to the input image.

3. The watermark is embedded in each location which has wavelet coefficient with magnitude higher than t1; the watermarked wavelet coefficient is given

where *wij* is the wavelet coefficient, *k* is a scaling parameter, *xij* is a watermark

2. All wavelet coefficients (barring the LL and HH components) of magnitude greater than t2 from a possibly corrupted watermarked image are selected. Note that by setting t2 > t1, we find that the robustness is increased, and some wavelet coefficients with magnitudes below t1 may become higher than t1 due

3.Wavelet coefficients with magnitude higher than t2 are used in the detection process; these detected values are correlated with the watermark values at the same locations. After this correlation process, a yes or no answer will be given

Another watermarking method operating upon significant coefficients within the wavelet domain was presented by Miyazaki et al. [9]. This method takes a threelevel wavelet transform of the image to be watermarked and inserts the watermark

ð5Þ

The embedding algorithm can be summarized in the following steps:

**4.1 Dugad's method**

*4.1.1 Embedding algorithm*

by Eq. (5):

*4.1.2 Detection algorithm*

to image manipulations.

**5. Non-blind watermarking**

**101**

as to the presence of the watermark.

access to the original uncorrupted image.

*Blind Wavelet-Based Image Watermarking DOI: http://dx.doi.org/10.5772/intechopen.88131*

or more details in the image.

value, and is the watermarked wavelet coefficient.

1. The watermark is regenerated using the known seed value.

The method proposed by Dugad et al. [3] has overcome the problem of "order sensitivity." It has some advantages such as an improved resistance to attacks on the watermark, an implicit visual masking utilizing the time-frequency localization property of the wavelet transform, and a robust definition for the threshold, which validates the watermark.

The disadvantage of this method is using additive technique in watermarking. In this additive method, the detectors must correlate watermarked image coefficients with the known watermark to know if the image is marked or not. To solve this problem, it is important to correlate a large number of coefficients as possible, but it in turn requires the watermark to be embedded into many image coefficients at the embedding stage. This has the effect of increasing the amount of degradation in the marked image. Another drawback is that the detector can only tell if the watermark is present or absent. It cannot recover the actual watermark. Here, we present a new method to avoid these drawbacks. It is possible to use the advantages of the watermarking scheme by Dugad et al. [3] while avoiding the disadvantages. This can be achieved using the idea of a watermark with the same size as the original image in conjunction with adapted versions of scalar quantization insertion/detection method. The resultant watermarking system will be blind and based on quantization.

A watermark size has to be equal in size to the detailed sub-band in wavelet transform domain, and only significant coefficients will be used to embed watermark. Finally, this new method outperforms the previous method using quantization and a new watermark embedding process, not the additive one. After applying a comparable robustness performance, the watermarked images using our new method give less degradation than Dugad's scheme.

However, only a few of these watermark values are added to the host image. The watermark values are found in fixed locations; thus, the ordering of significant coefficients in the correlation process is not an issue for watermark detection. This gives the technique a value as the correlation process is sensitive to the ordering of significant coefficients, and if there is any change applied to the ordering, it will cause a poor detector response.

In Zolghadrasli's method that is based on the DWT [10], Gaussian noise is used as the watermark. Here the watermark is added to the significant coefficients of each selected sub-band depending on the human visual system (HVS)

characteristics. Any small modifications are performed to improve HVS model. This technique is non-blind as the host image is needed in the watermark extraction.
