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Digital Image Watermarking Based on DWT-DCT:Evaluate for a New Embedding Algorithm

Afroja Akter, Nur-E-Tajnina, and Muhammad Ahsan UllahDepartment of Electrical and Electronic Engineering

Chittagong University of Engineering and TechnologyChittagong-4349, Bangladesh.

Email: afroja a@yahoo.com, nitu08078@gmail.com, and ahsan@cuet.ac.bd

Abstract—The authenticity of content or matter is crucial fac-tors for solving the problem of copying, modifying, and distribut-ing the intellectual properties in an illegal way. Watermarkingcan resolve the stealing problem of intellectual properties. Thispaper considers a robust image watermarking technique based ondiscrete wavelet transform (WDT) and discrete cosine transform(DCT) called hybrid watermarking. The hybrid watermarkingis performed by two level, three level, and four level DWTfollowed by respective DCT on the host image. A new embeddingalgorithm (NEA) of digital watermarking is proposed in thispaper. The simulation results are compared with Cox’s additiveembedding algorithm and the NEA for additive white Gaussiannoise (AWGN) attack and without attack. Both algorithms usethe hybrid watermarking. The NEA gives 3.04dB and 9.33dBbetter pick signal to noise ratio (PSNR) compared to Cox’sadditive algorithm for the 4 level DWT for AWGN attack andwithout attack, respectively. Moreover, the NEA extracts themarked image 46 times better of Cox’s additive algorithm in2 level DWT with AWGN attack. That means, the NEA canembed larger marks and high quality marks extract from theembedded watermarking even attacking condition. Though theNEA is evaluated in this paper by comparing performances withCox’s algorithm, the performances of NEA will compare amongother algorithms like Gaussian sequence, image fusion, nonlinearquantization embedding with various attacking conditions in nearfuture.

Keywords— Digital watermarking, DWT, DCT, Cox’salgorithm, PSNR, MSE.

I. INTRODUCTION

Watermarking techniques can compliment encryption byembedding a secret imperceptible signal into the host signal insuch a way that the embedded signal always remains present.Figure 1 represents the embedding and extracting process ofdigital watermarking. When an watermark is inserted in a

Fig. 1. Embedding and extracting process of digital watermarking.

host signal with a known key followed by an algorithm, thenthis is known as embedding process of digital watermarking.And when watermark is recover from the watermarked signal

using host signal and the key is known as extracting processof digital watermarking. There are various algorithms fordigital watermarking. The success of the watermarking schemelargely depends upon the choice of the watermark structureand insertion strategy [1]. The quality of digital watermarkingcan measure with two distinct parameters: imperceptibilityand robustness. Imperceptibility is measured by PSNR of hostimage and embedded image in dB. Higher PSNR is desired asit means to hide the marked image efficiently. And robustnessis measured by correlation of the original mark image andrecovered mark image [1]–[5].

Depending on the need of the original image, watermarkingis classified to non-blind and blind watermarking. The require-ment of original image for detecting the watermark is knownas non-blind watermarking, while the blind technique does notrequire the original image. Another way to classify watermark-ing that is transform domain watermarking and spatial domainwatermarking. Early watermarking schemes were in the spatialdomain, where the watermark is added by modifying pixelvalues of the host image [4],[5]. Some of the spatial domainswatermarking approaches are based on the modification of theleast significant bit (LSB) of both: host and marked images[7] -[9], [13]. of an image based on the assumption thatthe LSB data are insignificant generally. The spatial domainwatermarking is easy to implement from a computational pointof view but too fragile to resist numerous attacks [1],[3],[5].In order to have more promising techniques, researches weredirected towards watermarking in the transform domain, wherethe watermark is not added to the image intensities, but tothe values of its transform coefficients. Then to get the wa-termarked image one should perform the transform inversely.This paper also follows the transform domain watermarking.Some of the transform based watermarking techniques usedare discrete cosine transform (DCT) [5], [6], [12] discretewavelet transform (DWT)[5], [6], [11]. The wavelet transformgenerates a data structure known as scale space representation.In this image representation, the high frequency signals areprecisely located in the pixel domain, while low-frequencysignals are precisely located in the frequency domain. Thespatial resolution of the wavelet transforms increases withfrequency. Therefore sharp edges which are located spatiallyand have a significant high-frequency content, can be seenin the detail sub-bands and form the contours of the image’s

3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014

objects. While the frequency resolution is independent of thefrequency in the DCT domain, it is inversely proportional tofrequency in the wavelet domain.

This paper presents a new digital watermarking algorithmbased on combination of two transforms: DWT and DCT. Herecomparative results for two level ,three level and fourth level ofDWT are present for both NEA and Cox’s additive algorithm.Watermarking is done by altering the wavelets coefficientsof carefully selected DWT sub-bands of a 512 × 512 grayscale host image after required level of DWT decomposition,followed by the application of the DCT transform on theselected sub-bands. A 32×32 gray scale mark image enrolledwith a key is transform to DCT and then embedded withthe host image for fourth level of DWT. Similarly 64 × 64and 128 × 128 mark image are used in case of three leveland two level of DWT. Here the original host image is usedfor extracting the mark image, thus this paper focused onnon-blind or informed digital watermarking technique. Hereit is expected that these evaluation helps to take decision toconsider the NEA over Cox’s additive algorithm for somecases.

In this paper a new embedding algorithm (NEA) is proposedwhich can perform efficiently in comparison with Cox’s addi-tive algorithm. In case of established Cox’s additive algorithmthe mark image is multiplied with the host image. Thereforeit’s impact is noticed on the watermarked image, whereas incase of new algorithm the mark image is multiplied with theabsolute deviation of the host image. Thus the watermarkedimage degrade very slowly from the original host image.Moreover this paper represents that the NEA gives 3.04dBand 9.33dB better pick signal to noise ratio (PSNR) com-pared to Cox’s additive algorithm for the 4 level DWT forAWGN attack and without attack, respectively. Although therobustness of the both algorithm is quite similar in case ofwithout AWGN attack but the significant result of robustness isnoticed at AWGN attack condition, as at all levels of DWT theNEA extracts the marked image several times better of Cox’sadditive algorithm. So the NEA performs better than Cox’sadditive algorithm. In section two of this paper the proposedembedding technique of the digital watermarking is focused.The extracting process of the digital watermarking is focusedon section three. General requirements and simulation resultsare focused on section four. For simulating purpose the valueof scaling factor α is taken as 1.

II. WATERMARK EMBEDDING PROCEDURE

This paper follows the embedding procedure based on DCTand DWT [11]. DWT can separate the host image in 4levels of sub-band. There are LL (high scale low frequencycomponents), HL (Horizontal low-scale, high-frequency com-ponents), LH (Vertical low-scale, high-frequency components),and HH (Diagonal low-scale, high-frequency components).The new embedding algorithm(NEA) uses up to 4 level DWTand embeds image in HL and LH sub-band of host image data.For convenience to discuss, this paper first focused on 4 levelDWT and then focused on 3 level and 2 level respectively.

Fig. 2. Flowchart of embedding technique.

A 512 × 512 image is taken as host image and a 32 × 32

Fig. 3. Four multi-resolution DWT Sub-bands of the original image in level2.

image is taken as mark image in case of 4 level of DWT. A64× 64 and 128× 128 image is taken as mark image in caseof 3 level and 2 level of DWT respectively. Fig. 2 shows theimage embedding flowchart and its various operational stepsare described as follows:

Fig. 4. Four selected Multi-resolution DWT coefficient sets of the host imagein level 3.

Step 1 Perform DWT on a 512×512 host image to decom-pose it into four non-overlapping multi resolution

coefficient sets: LL1, HL1, LH1, and HH1.Step 2 Perform DWT again on two HL1 and LH1 sub-

bands to get eight smaller sub-bands and choose twocoefficient sets: HL12, and LH22 as shown in Fig.3.

Step 3 Perform DWT again on two sub-bands: HL12 andLH22 to get eight smaller Sub-bands and choose twocoefficient sets: HL13, LH23 as shown in Fig. 4.

Fig. 5. Four selected Multi-resolution DWT coefficient sets of the host imagein level 4.

Step 4 Perform DWT again on two sub-bands:HL13 andLH23 to get eight smaller Sub-bands and choose twocoefficient sets: HL14, LH24 as shown in Fig. 5.

Step 5 Divide coefficient sets: HL14, LH24, into 4 × 4blocks.

Step 6 Perform DCT to each block in the chosen coefficientsets (HLLL14, LHLL24).

Step 7 Re-formulate the grey-scale watermark.Step 8 Using a key scrambling the gray scale mark image.Step 9 Transform the image into DCT.Step10Embedding is done with a selected scaling factor

α with the Cox’s additive algorithm [2] and a newalgorithm respectively as follows in (1) and (2):

f ′(m,n) = f(m,n)(1 + α× w) (1)

f ′(mn) = f(m,n) + α× wf(m,n)− favg(m)√|f(m,n)2 − favg(m)2|

Where, f(m,n) is host image. favg(m) is the row-wise meanvalue of the host image. w is watermark image and f ′(m,n)is embedded image.

Now if step 4 and step 5 are removed and then rest ofthe step taken as before then it become a 3-level DWT-DCTbased digital watermarking technique. At this time the markimage become a 64×64 image and the mark image inserted inHLLL13 and LHLL23 after subdividing the HL13 and LH13into a 4×4 sub-blocks. Similarly if step 3, step 4 and step 5 areremoved from previous procedure and a 128×128 mark imageis inserted on the host image after step 2, then it’s become a

2-level DWT -DCT based digital watermarking technique. Atthis case HL12 and LH22 are subdivided into a 4× 4 blocksand a 128×128 mark image inserted in HLLL12 and LHLL22.

III. WATERMARK EXTRACTION PROCEDURE

Figure 6 shows the image extracting flowchart and itsvarious operational steps are described as follows:

Step 1 Perform DWT on the pre-filtered Watermarked imageto decompose it into four non-overlapping multi-resolution coefficient sets: LL1, HL1, LH1 and HH1.

Step 2 Perform DWT again on two sub-bands HL1 andLH1 to get eight smaller sub-bands and choose fourcoefficient sets: HL12 and LH22 as shown in Fig. 3.

Step 3 Perform DWT again on four sub-bands:HL12 andLH22 to get eight smaller sub-bands and choose twocoefficient sets: HL13 and LH23 as shown in Fig. 4.

Step 4 Perform DWT again on two sub-bands:HL13 andLH23 to get eight smaller sub-bands and choose twocoefficient sets: HL14 and LH24 as shown in Fig. 5.

Step 5 Divide two coefficient sets: HL14 and LH24 into 4×4 blocks.

Step 6 Perform DCT on each block in the chosen coefficientsets (HLLL14 and LHLL24).

Fig. 6. Flowchart of Extraction technique.

Step 7 Extraction is done with the selected scaling factor ′α′

using Cox’s additive algorithm and new algorithmrespectively as follows in (3) and (4):

w′ = f ′(m,n)− f(m,n)

f(m,n)× α(3)

w′ =[f ′(m,n)− f(m,n)]

√|f(m,n)2 − favg(m)2|

α[f(m,n)− favg(m)](4)

Where w′ is extracted watermark image.Step 8 Inverse DCT can perform.

Step 9 Using similar key the watermark image can extractform scrambled image.

IV. PERFORMANCE EVALUATION

Here for evaluation two performances has been tested, oneis imperceptibility and another one is the robustness of themark image. Here in this regard a 512× 512 image has beentaken as a host image and a 32 × 32 image taken as a markimage.

A. Imperceptibility:

Imperceptibility means that the perceived quality of thehost image should not be distorted by the presence of thewatermark. As a measure of the quality of a watermarkedimage, the peak signal to noise ratio (PSNR) is typically used.Pick signal to noise ratio (PSNR) [10] is a better test since ittakes the signal strength into consideration (not only the error).Equation (5) below describes how this value is obtained.

PSNRdB = 10× log10(MAX2

= 20× log10(MAX√MSE

B. MSE:

Mean squared error (MSE) [7] is one of the earliest tests thatwere performed to test if two pictures are similar. A functioncould be simply written according to equation (6) as below:

MSE =1

m∑k=1

n∑l=1

(f(k, l)− f ′(k, l))2 (6)

Where f(m,n) is host image and f ′(m,n) is watermarkedor embedded image.

C. Robustness:

The robustness of a watermark method can be evaluated byperforming attacks on the watermarked image and evaluatingthe similarity of the extracted message to the original one[10]. This paper measured the similarity between the originalwatermark and the watermark extracted from the attackedimage using the correlation value given below:

Corr. =

∑j{w(i,j)−wavg(i)}×{w′(i,j)−w′avg(i)}√(∑

∑j{w(i,j)−wavg(i)}2

)(∑i

∑j{w′(i,j)−w′avg(i)}2

Here Corr. means Correlation value of the original watermarkand recovering watermark image. The simulation results aregiven as follows for NEA and for Cox’s additive algorithmfor scaling factor α =1 in both cases. In Fog. 7 a 512 × 512host image is shown and 32 × 32 mark image is representsby Fig. 8. Fig. 9 shows the PSNR for NEA is 36.52 dB andMSE is 14.49, whereas the Cox’s additive algorithm gives thePSNR is 27.19dB and MSE is 124.10 which is shown in Fig.10 in case of 4 level DWT. The Fig. 11 shows the PSNR forNEA is 30.21 dB and MSE 61.90 with AWGN attack and theFig. 12 shows the PSNR for Cox’s additive algorithm is 27.17

Fig. 7. Original hostimage.

Fig. 8. Original mark-image.

Fig. 9. Watermarked image for NEA algorithm (PSNR=36.52dB, MSE=14.49)

dB and MSE is 124.27 with AWGN attack for 4 level DWT.Fig. 13 shows the recovered image for both NEA and Cox’sadditive algorithm for 4 level DWT. It is clear that the NEAcan hide the mark image properly without changing the viewof the host image. On the other hand, Cox’s additive algorithmfully changes the view of the host image. This might be betterwhen α value is less than 1. After recovering the mark imagefrom embedded image, the NEA gives quite good image thanthe recovered image quality of the Cox’s additive algorithm.The recovered image quality is better when AWGN attack isdone (see Fig. 13). Fig. 13 also shows the robustness of themark image at various conditions. A comparative result ofdifferent levels i.e. for level 4, 3 and 2 of DWT are shown infig. 14 and fig. 15 for both algorithms. From Fig. 14 it canbe said that PSNR value varies largely for lower level and theimpact of the mark image in watermarked image also increaseswith decreasing level for both algorithms. But PSNR degrades

Fig. 10. Watermarked image for Cox’s additive algorithm (PSNR=27.19dB,MSE= 124.10).

Fig. 11. Watermarked image for NEA algorithm with AWGN attack (PSNR=30.21dB, MSE= 61.90).

Fig. 12. Watermarked image for Cox additive algorithm with AWGN attack(PSNR=27.17dB, MSE=124.75.

less for NEA than Cox’s additive algorithm. The NEA gives3.04dB and 9.33dB better pick signal to noise ratio (PSNR)compared to Cox’s additive algorithm for the 4 level DWT andit is 1.28 dB and 2.44dB better in case of 3 level of DWT,and 1.05dB and 1.94dB better PSNR in case of 2level of DWTfor AWGN attack and without attack, respectively. Moreover,the NEA extracts the marked image 46 times better of Cox’sadditive algorithm in 2 level DWT with AWGN attack and

Fig. 13. The recovered image using NEA and Cox’s additive algorithm. (a)Extracted mark image without attack for NEA with correlation value of 1.(b) Extracted mark image without attack for Cox’s additive algorithm withcorrelation value of 1. (c) Extracted mark image with AWGN attack for NEAwith correlation 0.8. (d) Extracted mark image with AWGN attack for Cox’sadditive algorithm with correlation 0.1.

Fig. 14. PSNR value at different levels of DWT for both algorithm.

Fig. 15. Correlation value at different levels of DWT for both algorithm.

it is 7 times better for 4 level and around 2 times better incase of 3 level of DWT. From fig 15 it is clear that the NEAhaving significant robustness against attack than Cox’s additivealgorithm.

V. CONCLUSION

The two most common transform domains digital water-marking are discrete wavelet transform(DWT) and discretecosine transform (DCT) based digital watermarking. This

paper focused on a technique where these two are combining.This paper is proposed a new embedding algorithm (NEA).And this algorithm evaluated for 2 level, 3 level and 4 levelof DWT. Here also depicts comparative results of those levelsfor NEA and established Cox’s additive algorithm for identicalenvironment. Two measuring quantity pick signal to noise ratio(PSNR) and correlation are considered to measure impercepti-bility and robustness of digital watermarking respectively. Thispaper shows that the NEA gives 3.04dB and 9.33dB betterPSNR compared to Cox’s additive algorithm for 4 level DWTfor AWGN attack and without attack condition respectively.Furthermore the NEA extract the mark image 46 time s betterthan Cox’s additive algorithm at 2 level and 7 times betterwhen 4 level of DWT are considered. From the simulationresults it can be said that NEA can embed a larger size imagewith moderate imperceptibility level of embedded image atsignificant robustness of the mark image than the Cox’sadditive algorithm. The NEA might be promising compering toother embedding algorithms like Gaussian sequence algorithm,image fusion algorithm, nonlinear quantization. Moreover,there having some scope for further study on this algorithmwith various attacking conditions.

REFERENCES

[1] Yoseph Abatte, “Digital Image Watermarking”, Addis Ababa University,2005.

[2] Ingemar J. Cox, Joe Kilian, Tom Leighton, and Talal G. Shamoon, “Se-cure spread spectrum watermarking for multimedia”, IEEE InternationalConference on Image Processing, ICIP 97, volume 6, pages 1673 1687,Santa Barbara, California, USA, October 1997.

[3] Abrar Ahmed Syed, “Digital Watermarking”, the University of Texasat Arlington.

[4] Ali Assi, Engineering Education and Research Using MATLAB, ISBN978-953-307-656-0.

[5] Shital Gupta and Sanjeev Jain, “A Robust Algorithm of Digital ImageWatermarking Based on Discrete Wavelet Transform”, Department ofComputer Science & Engineering, LNCT, Bhopal.

[6] M.A. Mohamed and A.M. El-Mohandes, “Hybrid DCT-DWT Water-marking and IDEA Encryption of Internet Contents”, Electronics andCommunication Engineering, Faculty of Engineering-Mansoura Univer-sity, Mansoura, Dakhlia, Egypt.

[7] Ibrahim Nasir, Ying Weng, and Jianmin Jiang, “A New Robust Wa-termarking Scheme for Color Image in Spatial Domain”, School ofInformatics, University of Bradford, UK.

[8] R.G. Van Schyndel, A.Z. Tirkel, and C.F. Osborne, “A Digital Wa-termark”, Scientific Technology, 21 Walstab St, E. Brighton, 3187,Australia.

[9] S. Rohith and K.N. hari bhat, “A Simple Robust Digital Image Water-marking against Salt and Pepper Noise using Repetition Codes”, Na-garjuna College of Engineering and Technology, Bengaluru, Karnataka,India.

[10] Pooya Monshizadeh Naini, “Digital Watermarking Using MATLAB”,University of Tehran, Iran.

[11] Mei Jiansheng, Li Sukang, and Tan Xiaomei, “A Digital WatermarkingAlgorithm Based On DCT and DWT”, Nanchang Power Supply Com-pany, Nanchang, China.

[12] D.V.N. Koteswara Rao, Y. Madhuri, S.V. Rajendra Kumar, and Y.V.Suresh Babu, “Robust Image Watermarking using DCT & WaveletPacket Denoising”, Dept. of Electronics and Communication Engineer-ing, SACET,Chirala, International Journal of Scientific and EngineeringResearch Vol. 3, Issue 5, May-2012.

[13] Koushik Pal, G. Ghosh, and M. Bhattacharya, “A Novel DigitalImageWatermarking Scheme for Data Security Using Bit Replacement andMajority Algorithm Technique”, Institute of Radio Physics and Elec-tronics, University of Calcutta, Kolkata Indian Institute of InformationTechnology and Management, Gwalior India.

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