To Develop Blind Watermarking Biology Essay

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Slaven Marusic, David B.H.Tay et al presented a detailed study of biorthogonal wavelets in digital watermarking. In this paper they derived biorthogonal wavelet coefficients using Cohen-Daubechies- Feauveau (CDF) biorthogonal wavelet system.

Nagaraj.V.darwadkar Method

Nagaraj V. Dharwadkar et al [6], proposed a non-blind watermarking scheme for color images in RGB space using DWT-SVD in 2010. In this method, the watermark is embedded into cover image in RGB space. The combinations of discrete wavelet transform and singular value decomposition of blue channel is used to embed watermark. The singular values of different sub band coefficients of blue channel are modified using different scaling factors to embed the singular values of the watermark. The copy of the watermark is embedded into four sub band coefficients which are very difficult to remove or destroy [6].

Watermark embedding procedure

Step 1: Read the color image I of size NxN.

Step 2: Read the monochrome image X of size MxM and apply DWT on X to get D= {dij} of size MxM.

Step 3: Compute R, G and B channels from color image I of size NxN.

Step 4: Transform R, G and B channels into Y, I and Q channels of the color image.

Step 5: Compute third level DWT on Y channel to get the frequency components {HH1, HL1, LH1, {HH2, HL2, LH2, {HH3, HL3, LH3}}}.

Step 6: Embed the watermark frequency coefficients, starting from HH1 for each row select the frequency coefficients in descending order with respect their absolute values.

Step 7: Modify each frequency coefficient f of cover image to ij. If the subcomponent HH1 is insufficient to embed the complete watermark, then insert in the other coefficients in the order {HL1, LH1, {HH2, HL2, LH2, {HH3, HL3, LH3}}}.

Step 8: Save the location of the modified frequency coefficients into a key array K of size NxN. The key array consists of value 1 if the coefficient is modified otherwise 0.

Step 9: Replace by in decomposed y channel and compute inverse DWT of modified Y channel.

Step 10: combine modified Y channel with I and Q to get watermarked image.

Watermark extraction procedure

Step 1: Read the watermarked image of size of size NxN.

Step 2: compute,' and channels of the watermarked image.

Step 3: Transform these, and channels into, and channels.

Step 4: Compute third level DWT on channel to get the frequency components {HH1, HL1, LH1, {{HH2, HL2, LH2, {{{HH3, HL3, LH3}}}}.

Step 5: Compute third level DWT on Y channel of the un-watermarked image to get the frequency components {HH1, HL1, LH1, {HH2, HL2, LH2, {HH3, HL3, LH3}}}.

Step 6: Extract the watermark bits from the frequency subcomponents using the key array K as ij= (-)/α. If ij˃ T, then ij =1 other wise ij = 0.Whwere i= 1,2,3…M and j= 1,2,3…M.

2.3.2.2 Yanhong Zhang Method

Yanhong Zhang [7] proposed a blind watermark embedding/extracting algorithm using RBF neural network. In this method the DWT is used to overcome the blocking phenomenon problems in DCT. First, the original image is 4-scale level DWT transformed, and decided the watermarking strength according to HVS. When embedding watermark, a secret key is used to determine the watermark beginning location, and after that, embed and extract the watermark by using the trained RBF [7].

Watermark embedding procedure

Step 1: Transform the original image using DWT as is the LH4, HL4, HH4 sub-band coefficient.

Step 2: Select the beginning position of watermark embedding coefficient using the secret key.

Step 3: Quantize the coefficient of DWT, (i+key) by Q, as the input to the RBFN and then get the output.

Step 4: Embed the watermark according to the following equation ; Where is the watermark sequence, q is quantization value and is the coefficient of the watermarked image.

Step 5: Perform IDWT to get the watermarked image.

Watermark extraction procedure

Step 1: Transform the watermarked image by the DWT transform as with the sub band coefficients LH4, HL4, HH4.

Step 2: Quantize the DWT coefficient by Q as the input to the RBFN and then get the output.

Step 3: Extract the watermark using the following formula .

Step 4: Measure the similarity of the extracted watermark and the original watermark using the equation

Step 5: Use, threshold as a key to judge if there is an embedded watermark or not. If is larger than threshold and the location is equal to key, the watermark is affirmed.

2.3.2.3. He Xu, Chang Shujuan Method

He Xu, Chang Shujuan [10], proposed an adaptive image watermarking algorithm based on neural network. In this method, the watermarking signal is embedded in high frequency, which is in the lower frequency of original image by DWT joined with DCT. The ability of attracting is improved by pretreatment and retreatment of image scrambling and Hopfield network [10].

Watermark embedding procedure

Step 1: The watermarking signal is applied as the training signal input to the Hopfield network in order to finish the storage of the watermark.

Step 2: After doing scrambling transform, the watermark signal R is generated. The affine transform is used as scrambling transform, the key is scrambling times, and then the watermark pretreatment is completed.

Step 3: The low frequency sub- image LL is extracted from the original image by using the first order DWT transform. I will be gotten by DCT transform which process 8x8 block partitioning.

Step 4: The scrambling watermark sequence is embedded in high-frequency coefficients of the image I according to the equationin order to get. Where is embedding strength in the range 0 1.

Step 5: The IDCT is performed to get the low frequency sub-image LL which contains watermark and IDWT is performed to get the watermark image.

Watermark extraction procedure

Step 1: The detected image and original image are processed by first order DWT and T and I are gotten through DCT blocking phenomenon.

Step 2: Watermark is extracted through T and I input watermark detection module.

Step 3: The extracted watermark signal R is processed according to key inverse scrambling to get the watermark.

Step 4: The extracted watermark is applied as input to the Hopfield network and after data processing the watermark is extracted.

2.3.2.4. Charu Agarwal et al Method

Charu Agarwal et al, [34] proposed digital image watermarking in DCT domain using fuzzy inference system. In this method, Human Visual System (HVS) characteristics are modeled using a Fuzzy Inference System (FIS) for robust image watermarking. The fuzzy input variables corresponding to luminance sensitivity, edge sensitivity computed using threshold and contrast sensitivity computed using variance are fed to a FIS driven by ten fuzzy inference rules. The FIS produces a single output weighting factor which is used to embed a randomly generated normalized watermark with in the host image in the DCT domain. The signed image has good perceptual quality and is subject to stir mark image processing attacks. The high computed value of PSNR indicates robustness of the embedding algorithm. The watermark is extracted from the signed image using famous Cox's algorithm [34].

Watermark embedding procedure

Step 1: Cover image is divided into 8x8 blocks in spatial domain DCT is computed on all blocks.

Step 2: Compute edge sensitivity (threshold), luminance sensitivity and contrast sensitivity (variance) of all blocks of cover image.

Step 3: Supply these threshold, variance parameters as input to fuzzy inference system.

Step 4: Apply fuzzy inference rules to the fuzzy inference system and obtain the watermark weighting factor.

Step 5: Perform watermark embedding in low frequency DCT coefficients of cover image.

Step 6: Compute the IDCT to obtain the watermarked image.

Watermark extraction procedure

Step 1: Compute DCT of all 8x8 blocks of cover and watermarked (signed) images.

Step 2: Subtract the computed coefficients of original image from watermarked image.

Step 3: Recover the watermark using fuzzy inference system.

Step 4: Compare the recovered watermark with the original watermark using Sim(X, X*) parameter.

2.3.2.5. Sameh Oueslati et al Fuzzy Method

Samesh Oueslati et al [35], proposed a fuzzy watermarking system using the wavelet technique for medical images. In this method, an adaptive watermarking algorithm performed in the wavelet domain is proposed which exploits a human visual system (HVS) and a fuzzy inference system (FIS). HVS is adopted to further ensure the watermark invisibility. The FIS is utilized to compute the optimum watermark weighting function that would enable the embedding of the maximum energy and imperceptible watermark. For the purpose of security and robustness, a watermark sequence is embedded by selectively modifying the middle- frequency parts of the image [35].

Watermark embedding procedure

Step 1: Input the cover image and watermark image.

Step 2: Convert the watermark into a stream of binary data consisting of zeros and ones.

Step 3: Decompose the host image using Haar wavelet transform.

Step 4: Insert the data into wavelet coefficients, which have the largest values in middle frequency coefficients.

Step 5: Perform the inverse Haar wavelet transform to get the watermarked image.

Step 6: Display the watermarked image.

Watermark extraction procedure

Step 1: Input the watermarked image.

Step 2: Decompose the watermarked image using Haar wavelet transform.

Step 3: Select the wavelet coefficients which have largest values in middle frequency sub band.

Step 4: Compare the coefficients of cover image and watermarked image depending upon the location.

If the coefficient of embedding˃ original coefficient then the data store in it is 1

If the coefficient of embedding˂ = original coefficient then the data store in it is 0

Step 5: Display the recovered image.

2.3.2.6. Ming-Shing Hsieh Method

Ming- Shing Hsieh [36] proposed perceptual copyright protection using multi-resolution wavelet- based watermarking and fuzzy logic. In this method, an efficiently DWT-based watermarking technique is proposed to embed signatures in images to attest the owner identification and discourage the unauthorized copying. This technique is based on utilizing a context model and fuzzy inference filter by embedding the watermarks in the larger entropy coefficients of coarser DWT sub bands [36].

Watermark embedding procedure

Step 1: Sort the grey levels of watermark of size 'n' in ascending order to generate the sorted watermark.

Step 2: Decompose the host image into three levels with ten subbands of wavelet pyramid structure and choose a subband (HL3) to embed watermark.

Step 3: Calculate the weighted entropy of coefficients.

Step 4: Let the preset interval be and let t be the number of referenced coefficients used as a key to extract watermark without the host image. Coefficients with larger entropy are chosen from subband Where. The larger entropy coefficients make the watermark more robust and transparent. If then otherwise Where is used to get integer part of its argument. Let {} be the set of referenced coefficients and the coefficients to be embedded watermarks; {} is called the alternative coefficients. Sorting {} to generate {} called the sorted alternative coefficients.

Step 5: Quantize {} using a preset interval, which will extract the watermark W without the cover image.

Step 6: Embed watermark SW into subband HL3 using the equation

, To+T1+T2)/3=EnixT1.

Step 7: Save the symbol of embedded subband and perform IDWT to get the watermarked image.

Watermark extraction procedure

Step 1: Decompose watermarked image into three levels with ten subbands using DWT.

Step 2: Restore the scaling factor vi the symbol of embedded subband, symbol map of SCi, corresponsive map of Ci and SCi and corresponsive map of Wi and SWi.

Step 3: Extract the sorted watermarks by the proposed extracting watermarking algorithm.

Step 4: Rearrange the watermarks from corresponsive map of Wi and SWi to get the extracted watermark.

2.3.2.7. Soheila et al Method

Soheila Kiani et al [37], proposed Fractal based digital image watermarking using fuzzy C-mean clustering. In this method a new watermarking method is used to embed a binary watermark in to an image. The proposed method uses a special type of fractal coding that its parameters are contrast scaling the mean of rage block. Also, it utilizes the fuzzy C-mean clustering to address the watermark bits [37].

Watermark embedding procedure

Step 1: The fractal encoding is applied on the original image to produce fractal codes for all range blocks.

Step 2: Apply the fuzzy C-mean clustering on all the blocks and classify them into four groups.

Step 3: As per the centers calculated in previous step determine class A and B.

Step 4: For each bit of watermark:

If the bit is zero, P range blocks that their matched domain blocks are classified as A and selected randomly.

If the bit is one, P range blocks that their matched domain blocks are classified as B and selected randomly.

Step 5: Fractal decoding process is used to construct watermarked images.

Watermark extraction procedure

Step 1: Fractal coding is performed on watermarked images to generate fractal codes of all range blocks.

Step 2: The fuzzy C-mean clustering is applied on all blocks to classify them into four classes.

Step 3: According to the clusters class A and class B are determined.

Step 4: For all range blocks the watermark bits are determined according to the secret key as follows

If the most matched domain blocks belong to class A then the bit is zero.

If the most matched domain blocks belong to class B then the bit is one.

If the most matched domain blocks belong to class C or D then the bit is undetermined.

Step 5: Perform step 4 on all bits of watermarked image according to the secrete key.

These features have motivated to develop two new methods for watermarking in transform domain using Back Propagation Neural Network (BPNN) and Dynamic Fuzzy Inference System (DFIS).

RESEARCH OBJECTIVES

The objectives of this research work are as follows:

To explore digital image watermarking techniques using Back Propagation Neural Network and Dynamic Fuzzy Inference System in Discrete Wavelet Transform domain.

To develop watermarking techniques, which are imperceptible for unauthorized user, without affecting the original image quality.

To develop blind watermarking techniques so that the watermark can be detected without the original image.

To develop watermark techniques, which are robust against cropping, salt&pepper noise, rotation, JPEG compression etc., and having supremacy over existing watermarking methods.

PROBLEM STATEMENT

From the literature review, it is apparent that the digital image watermarking can be achieved by using either embedding the watermark directly into the image pixels of the cover image or into the transformed coefficients of the cover image. There are several requirements that the embedding method has yet to satisfy. Creating the robust and blind digital image watermarking methods is still a challenging task for researchers. These algorithms are robust against some attacks but not against most of them. Also, some of the current methods are designed to suit only specific application, which limits their wide spread use. Moreover, there are drawbacks in the existing algorithms associated with the watermark-embedding domain. These drawbacks vary from system to system. Watermarking schemes that modify the LSB of the data using a fixed magnitude PN sequence are highly sensitive to signal processing operations and geometric manipulations. This will limit their use in large number of applications.

To enable copyright protection and authentication, robust digital watermark can be embedded into multimedia contents imperceptibly. However, geometric distortions pose a significant threat to robust image watermarking because it can desynchronize the watermark information while preserving the visual quality. To overcome this, the robust digital image watermarking scheme using Back Propagation Neural Network in DWT domain is proposed, in which the geometrical effects such as cropping and rotation are minimized. Back Propagation Neural Network has good nonlinear approximation ability. It can establish the relationship between original wavelet coefficients and watermarked wavelet coefficients by adjusting the network weights and bias before and after embedding watermark. Owning to the use of neural network, we can extract watermark without the original signal and thus reduce the limit in practical applications. The correlation coefficient is further improved by using Dynamic Fuzzy Inference System. The primary novelty of this scheme is that the Mamdani type DFIS model is exploited in order to determine a valid approximation of a quantization step of each DWT coefficient. Furthermore, the HVS properties are modeled using biorthogonal wavelets to improve watermark robustness and imperceptibility. Finally, the results of BPNN and DFIS methods are compared.

CHAPTER SUMMARY

This chapter presented an overview of digital image watermarking. A survey is made on digital image watermarking and their limitations are also presented. Different domains of watermarking are explained in the next chapter.

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