New Strongly Robust DWT Based Watermarking Algorithm Computer Science Essay

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Abstract- In this paper we have presented two watermarking algorithms. First one is a new strongly robust strategy for right of first publication protection. This strategy is based on ‘Discrete Wavelet Transform ‘ , by implanting scrambled water line in HL subband at degree 3. Direct burdening factor is used in water line embedding and extraction procedure. This scheme consequences in exact recovery of water line with standard database images of size 512×512, giving Correlation Factor peers to 1. The Correlation Factor for different onslaughts like Noise add-on, Filtering, Rotation and Compression ranges from 0.90 to 0.95. The PSNR with burdening factor 0.02 is up to 48.53 dubnium. This is nonblind and embeds binary water line of 64×64 size. The 2nd technique is traditional method of watermarking. We besides tried to compare advanced strategy of first type with traditional method and recommended our advanced strategy.


It has become a day-to-day demand to make transcript, transmit and distribute digital informations as a portion of widespread usage of multimedia engineering in cyberspace epoch. Hence right of first publication protection has become indispensable to avoid unauthorised reproduction job. Digital image watermarking provides right of first publication protection to image by concealing appropriate information in original image to declare rightful ownership  . Robustness, Perceptual transparence, capacity and Blind watermarking are four indispensable factors to find quality of watermarking strategy . Watermarking algorithms are loosely categorized as Spatial Domain Watermarking and Transformed domain watermarking. In spacial sphere, water line is embedded by straight modifying pel values of screen image. Least Significant Bit interpolation is illustration of spacial sphere watermarking. In Transform sphere, water line is inserted into transformed coefficients of image giving more information concealment capacity and more hardiness against watermarking onslaughts because information can be spread out to full image [ 1 ] . Watermarking utilizing Discrete Wavelet Transform, Discrete Cosine Transform, CDMA based Spread Spectrum Watermarking are illustrations of Transform Domain Watermarking. The remainder of the paper is organized as follows: Section II focuses on study of bing digital image watermarking algorithms. Section III focuses on importance of Discrete Wavelet Transform. In subdivision IV, we have presented two watermarking strategies: In first strategy a new strongly robust DWT based algorithm is presented and 2nd strategy is traditional technique. Section V shows Experimental consequences after execution and Testing for both strategies. In subdivision VI, we have concluded and urge our foremost DWT based strategy.


In traditional watermarking attack some LSB based every bit good as watermarking methods with pseudo random generator are proposed [ 3 ] . In transform sphere methods, watermarking utilizing CWT, merely DWT, merely DCT or combined attack of DWT-DCT are proposed. In CWT, Calculating ripple coefficients at every possible graduated table is immense sum of work, and it generates a batch of informations. There is extremely excess information every bit per as the Reconstruction of the signal is concerned. Due to the attractive characteristics of Discrete Wavelet Transform, researches have been focused on DWT [ 15 ] . Wang Hongjun, Li Na have proposed a DWT based method [ 14 ] in which water line was embedded in in-between frequence coefficient utilizing I± as flexing factor with I± =I? |m| , where m is average value of all coefficients watermarking embedded. But this method does n’t supply adequate security. The method proposed in [ 14 ] utilizing DWT was extended in [ 15 ] to heighten security of algorithm by utilizing Arnold ‘s Transform pretreatment for water line. But this method can be extended to better PSNR and security degrees. As given in [ 16 ] , two stage water line implanting procedure was carried out utilizing DWT. Phase 1: Visible water line logo embedding, Phase 2: Feature extracted water line logo implanting. The algorithm was based on Texture Based Watermarking. A Integer Wavelet Transform with Bit Plane complexness Segmentation is used with more informations concealment capacity. [ 2 ] . But this method needs separate processing for R, G and B constituents of colour image. As given in [ 17 ] utilizing DWT, host image is decomposed into 3 degrees recursively. In flat one we get 4 sub sets. In degree 2, each subband of degree 1 is divided to 4 bomber sets to give entire 16 bomber sets. Finally, each subband of degree 2 is once more divided into 4 sub sets each to give entire 64 bomber sets. Then ‘ Generic algorithm ‘ was applied to happen the best subband for water line implanting to supply perceptual transparence and hardiness. But the procedure is excessively drawn-out and clip consuming. The common job with DCT watermarking is block based grading of water line image alterations scaling factors block by block and consequences in ocular discontinuity. [ 1 ] [ 6 ] . As given in [ 13 ] , J. Cox et. Al had presented ‘Spread spectrum based watermarking strategies ‘ , Chris Shoemaker has developed.

DWT has become research workers focus for watermarking as DWT is really similar to theoretical theoretical account of Human Visual System ( HVS ) . ISO has developed and generalized still image compaction criterion JPEG2000 which substitutes DWT for DCT. DWT offers mutiresolution representation of a image and DWT gives perfect Reconstruction of decomposed image. Discrete ripple can be represented as

For dyadic ripples a0 =2 and b0 =1, Hence we have,

Image itself is considered as two dimensional signal. When image is passed through series of low base on balls and high base on balls filters, DWT decomposes the image into sub sets of different declarations [ 11 ] [ 12 ] . Decompositions can be done at different DWT degrees.

At degree 1, DWT decomposes image into four nonoverlapping multiresolution bomber sets: LLx ( Approximate sub set ) , HLx ( Horizontal subband ) , LHx ( Vertical subband ) and HHx ( Diagonal Subband ) . Here, LLx is low frequence constituent whereas HLx, LHx and HHx are high frequence ( item ) constituents [ 7 ] [ 8 ] [ 9 ] .To obtain following coarser graduated table of ripple coefficients after degree 1, the subband LL1 is further processed until concluding N graduated table reached. When N is reached, we have 3N+1 subbands with LLx ( Approximate Components. ) and HLx, LHx, HHx ( Detail constituents ) where ten scopes from 1 to N. Three degree image decomposition is shown in Fig:1. Implanting water line in low frequence coefficients can increase hardiness significantly but maximal energy of most of the natural images is concentrated in approximate ( LLx ) subband. Hence alteration in this low frequence subband will do terrible and unacceptable image debasement. Hence water line is non be embedded in LLx subband. The good countries for water line embedding are high frequence subbands ( HLx, LHx and HHx ) , because human bare eyes are non sensitive to these subbands. They yield effectual watermarking without being perceived by human eyes. But HHx subband includes borders and textures of the image. Hence HHx is besides excluded. Most of the watermarking algorithms have been failed to accomplish perceptual transparence and hardiness at the same time because these two demands are conflicting to each other. The remainder options are HLx and LHx. But Human Visual System ( HVS ) is more sensitive in horizontal than perpendicular. Hence Watermarking done in HLx

Our watermarking methodologies

This strategy is betterment of algorithm presented in 2008 by Na Li et. Al, given in [ 15 ] utilizing Discrete Wavelet Transform with Arnold Transform. The betterment is made in following facets: The security degree is increased by presenting “ PN Sequence ‘ depending on Arnold cyclicity and depending on threshold value absolute difference of Arnold Transformed-Watermark-images is embedded. Alternatively of ciphering flexing factor related to intend value of coefficients of water line image, here straight appropriate weighting factor is selected. The Image decomposition is done with ‘Haar ‘ which is simple, symmetric and extraneous ripple.

Watermark Scrambling:

Watermark Scrambling is carried out through many stairss to better security degrees. Different methods can be used for image scrambling such as Fass Curve, Gray Code, Arnold Transform, Magic square etc. Here Arnold Transform is used. The particular belongings of Arnold Transform is that image comes to it ‘s original province after certain figure of loops. These ‘number of loops ‘ are called ‘Arnold Period ‘ or ‘Periodicity of Arnold Transform ‘ . The Arnold Transform of image is

( 3 )

Where, ( x, y ) = { 0,1, … ..N } are pixel co-ordinates from original image.

( , ) : corresponding consequences after Arnold Transform.

Cyclicity of Arnold Transform:

The cyclicity of Arnold Transform ( P ) , is dependent on size of given image. From equation: 3 we have,

( 4 )

( 5 )

If ( mod ( , N ) ==1 & A ; & A ; mod ( , N ) ==1 )

so P=N ( 6 )

Implanting Algorithm:

Measure 1: Decompose the screen image utilizing simple ‘Haar ‘ Wavelet into four nonoverlapping multiresolution coefficient sets: LL1, HL1, LH1 and HH1.

Measure 2: Perform 2nd degree DWT on LL1 to give 4 coefficients: LL2, HL2, LH2 and HH2.

Measure 3: Repeat decomposition for LL2 to give following degree constituents: LL3, HL3, LH3 and HH3 as shown in fig 1.

Measure 4: Find Arnold cyclicity ‘P ‘ of water line utilizing equation 6.

Measure 5: Determine ‘KEY ‘ where. Then bring forth PN Sequence depending on ‘KEY ‘ and happen the amount of random sequence say SUM.

Measure 6: If SUM & gt ; T where, T is some predefined Threshold value, so happen two scrambled images using Arnold Transform with KEY1 and KEY2, where, ,

Now, Take absolute difference of two scrambled images to give ‘Final Scrambled image ‘ .

Measure 7: If SUM & lt ; T, so use Arnold Transform straight to watermark image with ‘KEY ‘ to acquire ‘Final Scrambled image ‘ .

Measure 8: Add ‘Final Scrambled image ‘ to HL3 coefficients of screen image as follows:


Where, K1 is burdening factor, New_HL3 ( I, J ) is freshly calculated coefficients of level3, Watermark ( I, J ) is ‘Final Scrambled image ‘ .

Measure 9: Take IDWT at Level3, Level2 and Level1 consecutive to acquire ‘Watermarked Image.

Extraction Algorithm:

The proposed method is nonblind. Hence the original image is required for extraction procedure. The simple algorithmic stairss are applied are given below.

Measure 1: Decompose Cover image utilizing ‘Haar ‘ ripple up to 3 degrees to acquire HL3 Coefficients.

Measure 2: Decompose ‘Watermarked Image ‘ utilizing ‘Haar ‘ ripple up to 3 degrees to acquire HL3 ‘ .

Measure 3: Apply Extraction expression as follows:

Measure 4: Perform ‘Image Scrambling ‘ utilizing ‘Arnold Transform ‘ with ‘ KEY ‘ that we had used in implanting procedure to retrieve the Watermark.

Figure: 2 Watermark Embedding

Figure: 3 Watermark Extraction

This spacial sphere, watermarking is traditional strategy of watermarking. Here water line is embedded by straight modifying pel values of screen image as given below.

Watermark Embedding

Measure 1. Read grey scale Cover Image and Watermark.

Step2.Consider double star of pel values of Cover Image and do it ‘s n Least Significant Bits 0

e.g. For n=4, Binary of 143= & gt ; 10001111 and Making 4 LSB 0 = & gt ; 10000000= & gt ; 128 is denary equivalent.

Measure: 3 Consider double star of pel values of Watermark and right displacement by K spots where k=8-n. For n=4, K will be 4. Binary of 36= & gt ; 100100 and after right displacement by 4: 000010= & gt ; 2 is denary equivalent

Measure 4: Add consequence of measure 1 and step 2 to give watermarked image. E.g. Add 128+2= & gt ; 130. This gives pixel value of watermarked image= & gt ; 10000010

Figure: 4 Pixel of Cover image ( Original Image ) , Watermark,

Watermarked Image and Extracted Watermark

Watermark Extraction:

Take pels of watermarked Image and left displacement by K spots where k=8-n. e.g. Left displacement by 4= & gt ; 00100000 = & gt ; 32. This gives pels of Extracted Watermark. The sample values of Pixel of Cover image, Watermark, Watermarked_Image and Extracted Watermark are shown in fig.4.

Experimental results after implementation and testing

The undertaking is implemented in Matlab and standard database images with 512×512 sizes as screen image and 64×64 size binary water line images are used for proving. The public presentation Evaluation is done by two public presentation rating prosodies: Perceptual transparence and Robustness.

Perceptual transparence means sensed quality of image should non be destroyed by presence of water line. The quality of watermarked image is measured by PSNR. Bigger is PSNR, better is quality of watermarked image. PSNR for image with size M x N is given by:

Where, degree Fahrenheit ( one, J ) is pixel grey values of original image. degree Fahrenheit ‘ ( I, J ) is pixel grey values of watermarked image.

MaxI is the maximal pixel value of image which is equal to 255 for grey graduated table image where pels are represented with 8 spots. Robustness is step of unsusceptibility of water line against efforts to take or destruct it by image alteration and use like compaction, filtering, rotary motion, grading, hit onslaughts, resizing, cropping etc. It is measured in footings of correlativity factor. The correlativity factor measures the similarity and difference between original ‘watermark and extracted water line. It ‘ value is by and large 0 to 1. Ideally it should be 1 but the value 0.75 is acceptable. Robustness is given by:

Where, N is figure of pels in water line, wi is original water line, Wisconsin ‘ is extracted water line.

Here, we are acquiring PSNR 48.53 dubnium and =1, for burdening factor K1=0.02. The PSNR and for ‘standard database images ‘ with coeresponding trial image and recovered water lines are shown in Table 1. The grey scale ‘lena ‘ image is tested for assorted onslaughts given in Table 2. Here, we are acquiring within scope of 0.90-0.95 for assorted onslaughts. This shows that ‘watermark recovery ‘ is satisfactory under different onslaughts.

K1=0.07, ‘Lena ‘ image, size 512×512


First strategy presented here is a new strongly robust ‘Digital Image Watermarking ‘ with increased security degrees and bring forthing exact recovery of original water line for standard image database, giving correlativity factor peers to 1 and PSNR up to 48.53 dubnium. Experimental consequences have demonstrated that, this technique is really effectual back uping more security. As per ISO ‘s norms, the still Image Compression criterion JPEG2000 has replaced Discrete Cosine Transform by Discrete Wavelet Transform. This is the ground why more research workers are concentrating on DWT, which we have used for execution. The presented ‘Digital Image Watermarking ‘ methodological analysis can be extended for ‘color images and pictures ‘ for hallmark and right of first publication protection. Hence we are strongly urging our DWT based strategy which is presented here.


We are grateful to BCUD, University of Pune for supplying ‘Research Grant ‘ for the undertaking “ Transformed based strongly Robust Digital Image Watermarking ” in academic twelvemonth 2010-2011.

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