**4.1 Dugad's method**

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

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

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

method give less degradation than Dugad's scheme.

cause a poor detector response.

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

However, only a few of these watermark values are added to the host image. The

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

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

each selected sub-band depending on the human visual system (HVS)

change due to image manipulations.

validates the watermark.

*Cyberspace*

quantization.

**100**

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