image watermarking on low frequency dwt using

Seminar Nasional Teknologi Informasi dan Komunikasi 2016 (SENTIKA 2016) Yogyakarta, 18-19 Maret 2016

ISSN: 2089-9815

IMAGE WATERMARKING ON LOW FREQUENCY DWT USING SINGULAR VALUE DECOMPOSITION AND DITHER QUANTIZATION Prajanto Wahyu Adi1, Farah Zakiyah Rahmanti2 Faculty of Computer Science, Universitas Dian Nuswantoro (UDINUS) Jalan Imam Bonjol, No.207, Semarang 50131 E-mail: [email protected], [email protected]

1,2

ABSTRACT One of the objectives in the development of watermarking methods is to improve the robustness. We propose the use of low frequency Discrete Wavelet Transform (DWT) sub band in Singular Value Decomposition (SVD) process. The largest singular value of each block of wavelet coefficients are quantized using dither quantization method. The goal is to improve the robustness of watermark image while maintaining the visual quality of watermarked image. Several attacks are such JPEG compression, JPEG2000 compression, Average Filtering, and Gaussian Filtering used to test the robustness. The experimental results show that the proposed method has higher robustness level than the previous method which perform SVD and dither quantization on image pixels value. The use of low frequency DWT sub band has successfully improved the performance of SVD and dither quantization in term of robustness. Keywords: watermarking, dwt, singular value decomposition, dither quantization Conversely, transform or frequency domain methods have more robustness against several attacks (Lai & Tsai 2010)(Rao V 2012). It transforms image pixels value in spatial domain into frequency value called coefficients. The coefficients value represents several features of the image. Transform domain watermarking has been widely developed, such as Discrete Cosine Transform or DCT (Patra et al. 2010)(Gujjunoori & Amberker 2013) and Discrete Wavelet Transform or DWT (Lai & Tsai 2010)(Li et al. 2015). DCT is a widely used method, but it will causes significant degradation on watermarked image when embedding watermark with large size (Botta et al. 2016). On the other hand, DWT have multilevel resolution of image features that able to the robustness and the quality of watermarked image (Botta et al. 2016). This paper proposes a hybrid watermarking method on low frequency DWT coefficients, which able to improve the robustness while preserving the image quality as well.

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INTRODUCTION Digital watermarking is a method that is widely used in copyright protection of digital medium (Singh et al. 2015). It provides a solution for copyright infringement due to neglect of ownership in the process of copying media(Makbol & Khoo 2014). Digital watermarking is able to provide proof of ownership on digital media through a process of concealment of rights information into the medium. In its development, digital watermarking has been widely used in broadcast monitoring, authentication, and fingerprinting (Makbol & Khoo 2014). Nowadays, digital image become the most popular medium in watermarking. Watermarking consists of two main processes called embedding and extraction. Embedding is a process of hiding information called ‘watermark’ into host image, which will produce watermarked image. Extraction is a process of retrieving watermark from watermarked image. Watermarking methods can be classified into several types. Based on the resistance to attack, watermarking can be divided into robust and fragile watermarking. Robust watermarking is able to resist variety of image processing attacks, while fragile watermarking is highly vulnerable to such attacks. The other classification of watermarking is based on the reversibility of host medium used. Watermarking method that can restore the host medium after extraction process is known as reversible watermarking, otherwise if the host medium cannot be restored is called as irreversible watermarking. Alternatively, the most widely used classification in watermarking is based on its domain. The spatial domain methods have low computational complexity due to its scheme that embeds watermark into image pixel value, but it is vulnerable to attacks.

2. 2.1

PRELIMINARIES Discrete Wavelet Transform Discrete Wavelet Transform is a multilevel transformation method. It decomposes an image into four wavelet sub bands as shown in Figure 1. The wavelet sub bands consist of LL, HL, LH, and HH. LL is a low frequency sub band, which is top-left side of the wavelet sub band. It obtained through Low Pass Filtering (LPF) in both row and column directions. This sub band contains approximate value of an image, which is the most significant part of the image. LL has highest robustness level among all wavelet sub bands, it able to maintains information therein (Abu et al. 2014)(Adi et al. 2015).

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columns rows LPF

LPF 21

(Approximate)

12

LL

12

HL

(Horizontal)

12

LH

(Vertical)

12

HH

(Diagonal)

columns HPF

columns LPF

rows HPF

21

columns HPF (a)

Level 2

Level 1

(b) Figure 1. Discrete Wavelet Transformation: (a) Decomposition scheme (b) DWT Level on an image The top-right part of wavelet sub band represents the horizontal detail of the image called HL. It is generated from LPF in row order followed by High Pass Filtering (HPF) in column order. Conversely, LH is resulted from process of HPF in row order and LPF in column order. It depicts vertical element of the image and located at the bottom-left side of wavelet sub band. The last part of wavelet sub band is HH, which is the diagonal feature of the image. This sub band contains high wavelet coefficient, hence HH is vulnerable to attacks. DWT is able to decompose an image into multilevel wavelet sub bands as shown in Figure 1(b). The next level of decomposition is generally performed in LL to get the higher level of wavelet sub band (LL2, HL2, LH2, and HH2) and so on. The highest level of wavelet decomposition is when it reaches a single coefficient value.

[U S V] = SVD(X)

(1)

X = U*S*VT

(2)

where, VT is a conjugate transpose of matrix V. S is a diagonal matrix with large singular value contains in its diagonal entries. U and V are complex or real unitary matrices, such that U*UT and V*VT will results in identity matrix. 3.

PROPOSED METHOD This paper proposes the use of SVD on LL sub band in order to increase robustness of watermarked image. The singular value of LL is then quantized using dither quantization to insert the watermark image as shown in Figure 2. 3.1 Watermark Embedding 1. Decompose the host image into LL, HL, LH, and HH using 1 level of DWT 2. Divide LL into 8x8 non-overlapping block k and apply SVD get matrices Uk, Sk , and Vk (3) [U k S kVk ]  SVDk ( LLk )

2.2

Singular Value Decomposition Singular Value Decomposition (SVD) is used to decompose a matrix X into matrices U, S, and V. The diagonal matrix S contains singular values of the matrix X. While, the orthogonal matrices U and V contains the left and right singular values of matrix X respectively.

where Sk is a diagonal matrices, Uk and Vk are the real unitary matrices of block k

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Host Image Level 1

ISSN: 2089-9815

Watermarked Image Level 1 DWT Level 1

DWT Level 1

SVD on each 8x8 blocks of LL

U

S

V

Dither Quantization

LL

HL

LH

HH

Inverse DWT

Inverse SVD

S’

U

LL’

HL

LH

HH

V

LL

HL

LH

HH

SVD on each 8x8 blocks of LL

U

S

V

Watermark Image Quantization Table (Key)

Dither Quantization Embedding

Extraction

Watermark Image

Figure 2. The Proposed Method 3.

Get the lower and upper bound of each quantization step (4) li  min S  n(i  2) ui  min S  n(i  1)

4. 5.

(5)

where n is size of quantization step, min S is minimum value of largest singular value S1k on whole blocks, li and ui are the upper and lower bound of quantization step i, for i=1, 2, …, q. Create a key from li and ui of each quantization step. Adjust S1k according to the bit value of watermark that will be embedded, for li ≤ Sb < ui li  u i  / 2  li  / 2 , if b  1 S1k   li  u i  / 2  u i  / 2 , if b  0

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3.2 Watermark Extraction 1. Decompose the watermarked image into LL, HL, LH, and HH using 1 level of DWT 2. Divide LL into 8x8 non-overlapping block k and apply SVD get matrices Uk, Sk , and Vk 3. Get the key to extract watermark image 4. Get the watermark bit value on each block according to the key used, for li ≤ S1k < ui 1 , if bk   0 , if

li  S1k  li  ui  / 2

li  ui  / 2  S1k  ui

(8)

where bk is the k-th bit value on block k, and S1k is the largest singular value of block k. If S1k

(6)

out of range of quantization table, then bk = 0.

where S1k is the adjusted singular value of block k, and b is the bit value Apply inverse SVD on each block to get modified sub band LL’ (7) LLk  U k S kVkT

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EXPERIMENT RESULTS Six standard grayscale images within size of 512x512 pixels are used as host images and a binary image is used as watermark image as shown in Figure 3. The watermark embeds using step size of 60 to get finest result (Mohan & Kumar 2008). In our paper, the previous study by Mohan and Kumar (2008) that used SVD in spatial domain is used as comparison method.

where VkT is the conjugate transpose of Vk 7. Reconstruct the watermarked image from LL’, HL, LH, and HH using Inverse DWT.

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(a)

(b)

(c)

(d)

(e)

(f)

(g) Figure 3. The host images: (a) Baboon, (b) Elaine, (c) Jet, (d) Peppers, (e) Sailboat, (f) Truck; and the watermark image (g) UDINUS Logo 4.1

Performance Measurements In our paper, the imperceptibility or watermarked image qualities are measured using Peak Signal to Noise Ratio (PSNR) along with the Structural Similarity (SSIM) by (Wang et al. 2004). (2 x  y  C1 )(2 xy  C2 ) (9) SSIM ( x, y)  2 (  x   y2  C1 )( x2   y2  C2 ) where, µ is the mean value, σ2 is the variance, and σxy is the covariance of x and y. 1 M (10) MSSIM ( X , Y )  SSIM ( x , y ) M

 j 1

j

N

NC 

W W  p

p 1

N

p

(11)

N

W W  p 1

2 p

p 1

2

p

where, Wp and W p are the reference watermark and degraded watermark values at pixel p respectively. N is the number of pixels. 4.2

Imperceptibility Table 1 shows the qualities of watermarked images in both PSNR and SSIM measurements. The both method has acceptable PSNR value above 38dB (Lee et al. 2012). The SVD method has higher quality than the proposed method in PSNR measurement as shown in Figure 4. But the SSIM measurement in Figure 5 shows that the proposed method is outperformed the previous method.

j

X and Y are the host image and watermarked images respectively. xj and yj are the pixel value at j-th local block, and M is the number of local blocks of the image. The Normalized Correlation (NC) used to measure the robustness of watermark image under several attacks.

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Table 1. Watermarked Image Qualities Images

PSNR

Table 2. Robustness against Compressions

SSIM

SVD

Proposed

SVD

Proposed

Baboon

46.6586

41.7375

0.9990

0.9995

Elaine

45.6226

39.9956

0.9971

Jet

46.0016

40.1932

0.9966

Peppers

44.8163

38.8292

Sailboat

45.5120

Truck

47.9231

ISSN: 2089-9815

Images

SVD

Proposed

JPEG

JPEG2000

JPEG

JPEG2000

Baboon

0.7543

0.7076

0.9313

0.8816

0.9985

Elaine

0.8064

0.9463

0.9788

0.9952

0.9979

Jet

0.8023

0.9900

0.9798

0.9990

0.9951

0.9970

Peppers

0.8752

0.9748

0.9971

1.0000

40.0640

0.9972

0.9985

Sailboat

0.8092

0.9487

0.9809

0.9933

42.8141

0.9982

0.9991

Truck

0.6543

0.8785

0.8792

0.9486

Figure 4. Watermarked Image Quality in PSNR

Figure 6. Robustness against Compressions Table 3. Robustness against Filterings Images

SVD

Proposed

AF

GF

AF

GF

Baboon

0.6410

0.8042

0.7814

0.9117

Elaine

0.8730

0.9500

0.9243

0.9366

Jet

0.8186

0.9051

0.8556

0.9273

Peppers

0.8276

0.9329

0.9077

0.9648

Sailboat

0.7276

0.8904

0.7850

0.9599

Truck

0.7618

0.9197

0.8865

0.9200

Figure 5. Watermarked Image Quality in SSIM 4.3

Robustness The next experiment is examined the robustness of the watermark image against several image processing attacks. The watermarked image tests under compression and filtering attacks that are widely used in watermarking. Standard JPEG compression with quality factor of 50 and JPEG2000 with compression ratio of 5 are used in compression test. While, the Average Filtering (AF) with the default filter size of 3x3, and Gaussian Filtering with the default sigma value of 0.5 and filter size of 3x3 are used in filtering attacks.

Figure 7. Robustness against Filtering

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Table 4. Extracted Watermark Images Images Methods

Attacks Baboon

Elaine

Jet

Peppers

Sailboat

Truck

JPEG JPEG2000 SVD Average Filtering Gaussian Filtering JPEG JPEG2000 Proposed Average Filtering Gaussian Filtering

Table 2 and Figure 6 show that the proposed method has better robustness level compared to previous method in terms of JPEG and JPEG2000 attacks. Moreover, in filtering test using Average and Gaussian filtering, our method is better as well as shown in Table 3 and Figure 7. The extracted watermark images from such attacks are presented in Table 4. It shows that the extracted watermark of the proposed method has better visual quality than the previous method.

image. Several attacks are such JPEG compression, JPEG2000 compression, Average Filtering, and Gaussian Filtering are used to test the robustness. The experimental results show that the proposed method has higher robustness’ level than the previous method by Mohan and Kumar which perform SVD and dither quantization on image pixels value. The use of low frequency DWT sub band has successfully improved the performance of SVD and dither quantization in term of robustness.

5.

ACKNOWLEDGEMENT The authors would like to thank to Institute of Research and Community Services (LPPM), Universitas Dian Nuswantoro for providing financial support on this study by Research Grants Scheme (021/A.35-02/UDN.09/X/2015)

ANALYSIS AND DISCUSSIONS Due to the general wavelet transformation, the DWT is able to perform image down sampling on its decomposition process as shown in Figure 1. This down sampling scheme will yield smaller size of watermark image with higher level of density in dither quantization step. The increasing density level will improve the robustness of the watermark image under various attacks such JPEG, JPEG2000, Average Filtering, and Gaussian Filtering. The watermark is embedded in LL sub band, which have lowest frequency in wavelet sub bands. Low frequency wavelet is able to preserve the features residing in it from such compression and filtering attacks. Hence, embedding in LL will improve the robustness as well.

REFERENCES Abu, N.A., Adi, P.W. & Mohd, O., 2014. Robust Digital Image Steganography within Coefficient Difference on Integer Haar Wavelet Transform. lnternational Journal of Video & Image Processing and Network Security (IJVIPNS), 14(02), pp.1 – 8. Adi, P.W., Rahmanti, F.Z. & Abu, N.A., 2015. High Quality Image Steganography on Integer Haar Wavelet Transform using Modulus Function. In 2015 International Conference on Science in Information Technology (ICSITech). pp. 79–84. Botta, M., Cavagnino, D. & Pomponiu, V., 2016. A modular framework for color image watermarking. Signal Processing, 119, pp.102–114. Available at: http://linkinghub.elsevier.com/retrieve/pii/S01

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CONCLUSION This paper proposes the use of low frequency wavelet sub band in Singular Value Decomposition (SVD) process. The largest singular value of each block of wavelet coefficients are quantized using dither quantization method. This aims to improve the robustness of watermark image while maintaining the visual quality of watermarked

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65168415002534. Gujjunoori, S. & Amberker, B.B., 2013. DCT based reversible data embedding for MPEG-4 video using HVS characteristics. Journal of Information Security and Applications, 18(4), pp.157–166. Available at: http://linkinghub.elsevier.com/retrieve/pii/S13 63412713000113 [Accessed October 2, 2014]. Lai, C.-C. & Tsai, C.-C., 2010. Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition. IEEE Transactions on Instrumentation and Measurement, 59(11), pp.3060–3063. Lee, Y.P. et al., 2012. High-payload image hiding with quality recovery using tri-way pixelvalue differencing. Information Sciences, 191, pp.214–225. Available at: http://dx.doi.org/10.1016/j.ins.2012.01.002. Li, C. et al., 2015. Dither modulation of significant amplitude difference for wavelet based robust watermarking. Neurocomputing, 166, pp.404– 415. Available at: http://linkinghub.elsevier.com/retrieve/pii/S09 25231215003380. Makbol, N.M. & Khoo, B.E., 2014. A new robust and secure digital image watermarking scheme based on the integer wavelet transform and singular value decomposition. Digital Signal Processing, 1(134), pp.1–14. Available at: http://dx.doi.org/10.1016/j.dsp.2014.06.012. Mohan, B.C. & Kumar, S.S., 2008. A Robust Image Watermarking Scheme using Singular Value Decomposition. Journal of Multimedia, 3(1), pp.7–15. Patra, J.C., Phua, J.E. & Bornand, C., 2010. A novel DCT domain CRT-based watermarking scheme for image authentication surviving JPEG compression. Digital Signal Processing, 20(6), pp.1597–1611. Available at: http://linkinghub.elsevier.com/retrieve/pii/S10 51200410000795. Rao V, S., 2012. A DWT-DCT-SVD Based Digital Image Watermarking Scheme Using Particle Swarm Optimization. In 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science. pp. 10–13. Singh, V., Kumar, R. & Ojha, A., 2015. Significant region based robust watermarking scheme in lifting wavelet transform domain. EXPERT SYSTEMS WITH APPLICATIONS, 42(21), pp.8184–8197. Available at: http://dx.doi.org/10.1016/j.eswa.2015.06.041. Wang, Z. et al., 2004. Image Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), pp.600–612.

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