Presentation on theme: "Multi resolution Watermarking For Digital Images Presented by: Mohammed Alnatheer Kareem Ammar Instructor: Dr. Donald Adjeroh CS591K Multimedia Systems."— Presentation transcript:
Multi resolution Watermarking For Digital Images Presented by: Mohammed Alnatheer Kareem Ammar Instructor: Dr. Donald Adjeroh CS591K Multimedia Systems
Overview Why multi resolution watermarking? Introduction Previous Work Multi resolution representation Multi resolution watermark embedding Watermarking extracting procedures Discussion Experimental results Conclusion Limitations Improvements/Suggestions
Why multi resolution watermarking? Image quality degradation, the low resolution rendition of the watermark will still be reserved within the corresponding low resolution components of the image.
Introduction Digital Watermark was proposed as a way to claim the ownership of the source and the owner. In order for watermarking to achieve the maximum protection, the watermarking should be:
Introduction Undeletable Perceptually invisible Statistically undetectable Resist to lossy data compression Resist to common image processing operations Unambiguous
Previous work They use watermark as a symbol or ID number which is comprised of a sequence of bits, and can only be detected by employing the “Detecting Theory”. The original image is subtracted from the image in question, and the similarity between the difference and specific watermark is obtained. The similarity is compared with a predefined threshold to determine whether the image in question contains the specific watermark.
Goal of this paper In this paper, the watermark is visually recognizable pattern, which can be extract instead of only detected and characterized the owner. So, they can use both the extracted pattern and the similarity measurement for determining whether the image is watermarked.
Restrictions for embedding Watermark imbedding concentrated in low frequencies Important Watermark features should be not be in high frequencies Watermarks high rez copies are mapped to high host frequencies Watermark needs to be randomly placed in the image so it is difficult to maliciously remove
1) Pyramid Structure of a binary watermark JBIG is used as a resolution reduction function of the watermark Low rez – high rez = differential watermark no loss of the binary watermark under such a reducing and reconstructing process.
2) Wavelet decomposition of the host image Wavelet transform is used to hierarchically decompose the host image into a series of successively lower resolution reference images and their associate detail images. At each level, the low resolution image and the detail images contain the information needed to reconstruct the reference image at next higher resolution level.
3) Pseudo Permutation To mix the inlay of the watermark for difficult extraction 2d pseudo-random number traversing Number each pixel in watermark in ascending order Then randomly order the numbers
4) Block Based image- dependent permutation To improve perceptional invisibility Pixels are conformed to a pattern of the host image
5) Modification of DWT Coefficients Watermarks are imbedded into a neighboring relationship on a per rez bases A residual mask is used to map watermark to transformed host Compute residual polarity between neighboring pixels Modifiy corresponding pixels of transformed host by adding or subtracting polarity
Watermarking extracting procedures We need both the original and the watermark images not only for obtaining the permutation mapping specified in image-dependent permutation, but also in computing the correlation with the extracted result as a watermark verification process. The extraction steps are the following:
Watermarking extracting procedures 1. Discrete wavelet transformation: Both the original image and the watermarked image are DWT transformed. 2. Generation of the residual results: By applying the residual masks during the embedding step we can generate the residual results for the detail images. 3. Extract the permuted differential watermarks: Perform the exclusive-or (XOR) operation on the residual results to obtain the permuted binary data.
Watermarking extracting procedures 4. Reverse the permutations: The image dependent permutation mapping can be obtain from the original detailed image and the differential watermark. Then, reverse the image-dependent permutation by the image-dependent permutation mapping, and followed with the pseudo-random permutation according to the predefined pseudo-random order.
Watermarking extracting procedures 5. Reconstruct the watermark: Reconstruct the higher-resolution layers to obtain the extracted watermarks. 6. Similarity Measurement: Similarity measurement of the extracted watermark and the referenced watermark can be defined as:
Discussion 1. User Key. A user key is used as additional feature that can be implemented to serve various embedding processes by using the same embedding technology. 2. Evaluation of Wavelet filters. Choice of wavelet filters is critical issue that affect the quality of the watermarked image and the robustness to compression attacks.
Conclusion with the characteristics of successive approximation, as a higher-resolution images are obtained, the higher resolution watermark will be extracted
Limitations This method can only work if the image is to be worked is ½ the size of the image to watermark. So, we have to place a serious restrictions on the type of watermark which can be used. Since the watermark can be any size, this method isn’t appropriate or reliable for variable size image which is used as a watermark. We used both the watermarked image and the original images. Other methods require only the watermark image in order to tell whether the image is embedded or not.
Limitations The evaluation of suitability of wavelet filters for invisible watermarking is under exploring.
Improvements/Suggestions They should extend their method to work for also color images. This paper was not giving any solution to any general form of image manipulation.