International Journal of Advances in Electrical and Electronics Engineering Available online at www.ijaeee.com & www.sestindia.org
173 ISSN: 2319-1112
Digital Image Watermarking Techniques: A Comparative Study Vishal Verma, Mrs. Jyotsna Singh Division of Electronics and Communication Engineering Netaji Subhas Institute of Technology, New Delhi, India
[email protected],
[email protected]
Abstract—Digital watermarking plays an increasingly important role for proving authenticity and copyright protection. The Internet is an ideal medium for selling digital goods it also makes redistribution of pirated files very easy. Digital watermarking can be used to insert invisible data into an object helping to track down pirate copies and to prove rightful ownership in a dispute. In this paper we present a detailed survey of existing image watermarking techniques. We classify the techniques based on different domains in which data is embedded. The performances of these generations arepresented in terms of subjective results. Keywords—bit plane slicing, discrete wavelet transform, peak signal to noise ratio, singular value decomposition, watermarking.
I. INTRODUCTION Digital media causes extensive opportunities for piracy of copyrighted material. Most multimedia file formats do not introduce any restriction on copying or manipulating multimedia objects and while the internet is an ideal medium for selling digital goods, it also makes redistribution of pirated files very easy. Images can be easily duplicated and distributed without owner’s consent. The ways and means are required to detect copyright violations and control access to these digital media. Digital watermarking plays an increasingly important role for proving authenticity and copyright protection. Unfortunately the currently available formats for image in digital form do not allow any type of copyright protection. A potential solution to this kind of problem is an electronic stamp or digital watermarking which is intended to complement cryptographic process The process of embedding the watermark into a digital data is known as Digital Watermarking. It embeds some marking information directly into the digital carrier (including multimedia, documents or software), but it is not easily noticed by human perception. Digital watermarking is a way of hiding a secret or personal message to provide copyrights and the data integrity. The concept of digital watermarking is also associated with the steganography. It is defined as covered writing, which hides the important message in a covered media while, digital watermarking is a way of hiding a secret or personal message to provide copyrights and the data integrity. It is a new approach, which is suitable for medical, military, and archival based applications. The embedded watermarks are difficult to remove and typically imperceptible, could be in the form of text, image. [1]. Fig.1 (a) & (b) shows a digital image watermarking system's principle setup. Watermarking can be described by two basic modules, one is to hide the message in data which is called watermark embeder and the other module is detection of watermark named as watermark detector or Extractor. The embedded watermark may be pseudorandom binary sequence, chaotic sequence, spread spectrum sequence or binary/gray scale image. In many cases there is an additional data item necessary for embedding or detection, such as a secret key. The kind of detection result depends on the watermarking application. In some cases the presence of a known watermark pattern is detected, in others a message of some kind (text, or even multimedia contents like images, audio etc.) is read. Digital image watermarking technique provides perceptibility. A watermarking system is of no importance to anyone if it degrades the cover image to the extent that it being useless, or highly distracting to its intended user. An ideal watermarked imaged should appear indistinguishable from the original image even if one uses highest quality equipment. The present paper describes computationally efficient spatial domain as well as spectral domain watermarking techniques and non blind watermark recovery Techniques. The paper is organized as follow: Section II outline various techniques used for image watermarking. Section III discusses the experimental results. This paper is concluded in Section IV.
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Vishal Verma and Mrs. Jyotsna Singh II. TECHNIQUES USED FOR WATERMARKING There are two types of methods used for watermarking. First one is spatial domain Method another one is transform domain method: A. Spatial Domain Method The spatial domain is the normal image space, in which a change in position of image is directly projected onto a change of position in space. Distances I (in pixels) correspond to real distances (in meters) in space. In this paper we will discuss LSB Plane Method. Watermark Embedding
Watermark Detection/Extraction
Figure1. Example of Watermark embedding and Detection or extraction
B. Transform Domain Method In these techniques the image is first transformed into spectral domain by the use of different transforms. In such techniques, the watermark is not directly embedded in the pixels of image, but to the values of its transform coefficients. Then inverse of the transform gives watermarked image. In the subsequent section we will be discussing various watermarking techniques implemented in transform domain such as discrete Cosine transform (DCT) and discrete wavelet transform (DWT). 2.1. Least Significant Bit Substitution Technique The best known Watermarking method that works in the spatial domain is the Least Significant Bit (LSB), which replaces the least significant bits of pixels selected to hide the information [2].Note that the human eyes are not very attuned to small variance in color and therefore processing of small difference in the LSB will not noticeable. The steps to embed watermark image are given below. A.
Embedding watermark
1.
Split the Cover image (Lena.bmp) into 8 bit planes from MSB to LSB.
2.
Similarly split the message (watermark image) into 8 bit planes (Baboon.bmp).
3.
Now remove the last four planes (LSB planes) of cover image as well as message image.
4.
Removed last four planes of Cover image are replaced with remaining upper four planes (MSB planes) of message image.
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175 Digital Image Watermarking Techniques: A Comparative Study 5.
Now combined the whole 8 planes (4 MSB planes of Cover image & 4 MSB planes of message image).
6.
Produced image will be watermarked image.
B.
Extracting watermark
1.
Split the watermarked image into 8 bit planes from MSB to LSB.
2.
Now remove the upper four planes (MSB planes) of watermarked image.
3.
Last four LSB planes will give the message (watermark) image.
2.2 Discrete Cosine Transform Technique The DCT allows an image to be broken up into different frequency bands, making it much easier to embed Watermarking information into the middle frequency bands of an image [3]. The middle frequency bands are chosen such that they have minimized they avoid the most visual important parts of the image (low frequency) without over-exposing themselves to removal through compression and noise attacks. A. Embedding watermark 1.
First divide the image into blocks by JPEG standard.
2.
Determine maximum message size based on cover object and block size.
3.
Check that the message isn't too large for cover image.
4.
Pad the message out to the maximum message size with ones
5.
Process the image in blocks. Encodes such that (5,2) > (4,3) when message(kk)=0 and that (5,2) < (4,3) when message(kk)=1
6.
Transform block using DCT.
7.
If message bit is black, (5,2) > (4,3).if (5,2) < (4,3) then we need to swap them. if message bit is white, (5,2) < (4,3) and if (5,2) > (4,3) then we need to swap them.
8.
Now we adjust the two values such that their difference = k, where k is a secret key for embedding watermark, which called minimum coefficient difference of DCT blocks.
9.
Transform block back into spatial domain. Move on to next block. At and of row move to next row. Which gives us watermarked image.
B. Extracting watermark 1.
Perform DCT transform on watermarked image and original host image.
2.
Subtract original host image from watermarked image.
3.
Multiply extracted watermark by scaling factor to display.
2.3 Discrete Wavelet Transform Technique The basic idea in the DWT for a one dimensional signal is the following. A signal is split into two parts, usually high frequencies and low frequencies. The edge components of the signal are largely to the high frequency part. The low frequency part is split again into two parts of high and low frequencies [4]. This process is continued an arbitrary number of times, which is usually determined by the application at hand.
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Vishal Verma and Mrs. Jyotsna Singh Figure2. DWT Decomposition with two levels
A. Embedding watermark 1.
Use one-level Haar DWT to decompose the cover image A into four subbands (i.e., LL, LH, HL, and HH). [LL, HL, LH, HH] = dwt2 (A,'haar')
2.
Find the dimension of LL plane.
3.
Resize the message image w with the dimension of LL plane.
4.
The scale factor is used to control the strength of the watermark to be inserted, W=α*w Where α denotes the scale factor.
5.
Now add this scaled watermark with LL plane. [LLNEW]= [LL] + [W]
6.
Obtain the watermarked image AW by performing the inverse DWT using one set of modified DWT coefficients and three sets of non-modified DWT coefficients.
B. Extracting watermark 1.
Use one-level Haar DWT to decompose the watermarked (possibly distorted) image A∗W into four subbands: LL’, LH’, HL’ and HH’.
2.
Obtained LL’ plane and subtract Previous LL Plane from this. [W’] = [LL’] - [LL].
3.
Rescale the image with security key α w = W’/α
4.
The Result of Step 3 gives the embedded watermark.
2.4 Singular Value Decomposition Technique SVD is a numerical technique used to diagonalize matrices in numerical analysis. It is an algorithm developed for a variety of applications. The main properties of SVD from the viewpoint of image processing applications are: 1) the singular values (SVs) of an image have very good stability, that is, when a small perturbation is added to an image, its SVs do not change significantly; and 2) SVs represent intrinsic algebraic image properties. In this section, we describe a watermark casting and detection scheme based on the SVD [5]. A. Embedding watermark 1.
Apply SVD to decompose the cover image A into Three matrixes (i.e., S, U and V). A=U*S*VT Where U and V are orthogonal matrices and D is a singular, diagonal matrix.
2.
Find the dimension in S matrix and resize the message image W with the dimension of S matrix.
3.
Modify the singular values of matrix S with the watermark image. SW = S + k*W. Where k denotes the scale factor. The scale factor is used to control the strength of the watermark to be inserted.
4.
Now apply the SVD on the SW matrix and find new singular value matrix SNEW. SW = UN*SN*VTN
5.
Obtain the watermarked image AW by performing SVD using modified SN singular value coefficients and non modified U and V coefficients.
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177 Digital Image Watermarking Techniques: A Comparative Study AW = U*SN*VT B. Extracting watermark 1.
Apply the transpose operation on the watermarked image AW. AWN = AWT
2.
Apply SVD on this modified matrix AWN. AWN = U*Sr*VT
3.
Now, find D as given equation: D = UN*Sr*VTN
4.
The embedded watermark is obtained by: W = (D - S) / k
2.5 Singular Value Decomposition and discrete Wavelet Transform Hybrid Technique We have implemented three different algorithms for watermarking using SVD-DWT hybrid technique. Method No.1 (Watermark applied on two subbands) [6] A. Embedding watermark 1.
Use one-level Haar DWT to decompose the cover image A into four subbands (i.e., LL, LH, HL, and HH).
2.
Apply SVD to LH and HL subbands, i.e., Ak = Uk*Sk*V kT,
k= 1, 2
Where k represents one of two subbands. 3.
Divide the watermark into two parts: W = W1 +W2, Where Wk denotes half of the watermark.
4.
Modify the singular values in HL and LH subbands with half of the watermark image and then apply SVD to them, respectively, i.e., Sk + αWk = UkW*SkW*V kTW. where α denotes the scale factor. The scale factor is used to control the strength of the watermark to be inserted.
5.
Obtain the two sets of modified DWT coefficients, i.e., A∗k = Uk*SkW*VkT,
6.
k= 1, 2
Obtain the watermarked image AW by performing the inverse DWT using two sets of modified DWT coefficients and two sets of no modified DWT coefficients.
B. Extracting watermark 1.
Use one-level Haar DWT to decompose the watermarked (possibly distorted) image A∗W into four subbands: LL, LH, HL, and HH.
2.
Apply SVD to the LH and HL subbands, i.e., A∗kW = U∗k *S∗kW *V ∗kT,
k= 1, 2
where k represents one of two subbands. D∗k = UkW S∗kW V kTW ,
3.
Compute
4.
Extract half of the watermark image from each subband, i.e., W∗k = (D∗k − Sk)/α,
5.
k = 1, 2.
k = 1, 2.
Combine the results of Step 4 to obtain the embedded watermark: W∗ = W∗1 +W∗2
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Vishal Verma and Mrs. Jyotsna Singh Method No.2 :( Watermark applied on all four subbands) [7] A. Embedding watermark 1.
Using DWT, decompose the cover image A into 4 subbands: LL, HL, LH, and HH.
2.
Apply SVD to each subband image:
ܣ = ܷ ܵ ்ܸ , k = 1,2,3,4,
where k denotes LL, HL, LH, and HH bands, and ߣ ,i=1,…,n are the singular values of ܵ . 3.
Apply SVD to the visual watermark: ் ܹ = ܷௐ ܵௐ ܸௐ ,
where λwi, i = 1,…,n are the singular values of ܵௐ . 4.
Modify the singular values of the cover image in each subband with the singular values of the visual watermark: ߣ∗ = ߣ + ߙ ߣ௪ , i = 1,…,n, and k = 1,2,3,4.
5.
Obtain the 4 sets of modified DWT coefficients:
∗ܣ = ܷ ܵ∗ ்ܸ , k = 1,2,3,4. 6.
Apply the inverse DWT using the 4 sets of modified DWT coefficients to produce the watermarked cover image.
B. Extracting watermark 1.
Using DWT, decompose the watermarked (and possibly attacked) cover image A*into 4 subbands: LL, HL,LH, and HH.
2.
Apply SVD to each subband image:
∗ܣ = ܷ ܵ∗ ்ܸ , k = 1,2,3,4 where k denotes the attacked LL, HL,LH and HH bands. 3.
Extract the singular values from each subband (ߣ௪ = ߣ∗ − ߣ )/ߙ , i = 1,…,n, and k = 1,2,3,4.
4.
Construct the four visual watermarks using the singular vectors: ் ܹ = ܷௐ ܵௐ ܸௐ , k = 1,2,3,4.
Method No.3 (Our Method- applying watermark on only one subband) A. Embedding watermark 1.
Apply one level Haar wavelet on the image A to decompose into four subbands LL, LH, HL and HH.
2.
Apply SVD on LL subband: A= ܷ ܵ ்ܸ .
3.
Apply SVD on watermark image: ܹ = ܷௐ ܵௐ ܸௐ் .
4.
Modifying the singular values of LL subband: ܵ௪ = ܵ + ݇ܵௐ
5.
obtain the modify LL subband with ܵ௪
6.
Apply the inverse DWT using the modified DWT coefficients to produce the watermarked image.
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179 Digital Image Watermarking Techniques: A Comparative Study B. Extracting watermark 1.
Apply Haar DWT to decompose the watermarked image AW into four subbands: LL, LH, HL, and HH.
2.
Now apply SVD decomposition on LL subband. ∗ ∗் ܣௐ = ܷ ∗ ܵௐ ܸ
∗ Find D: ܵ( = ܦௐ − ܵ )/݇
3.
Recover watermark: ܹ = ܷௐ ܸܦௐ்
4.
III. EXPERIMENTAL RESULTS Several experiments are presented to demonstrate the performance of the proposed approach. The gray-level images “Lena” of size 256 ×256 and “Cameraman” of size 128 × 128 are used as the cover image and the watermark, respectively. 3.1 Imperceptibility of watermark The imperceptibility of watermark image is qualitatively decided by visual artifacts in watermarked image. Different literatures have reported different metrics. As a quantitative measure, following metrics are used. The notations used are listed below. X(i,,j) : original image, X’(I,j) : Watermarked image, and Nt
: Size of image
(i) Mean Square Error (MSE) Mean Square Error between original image and watermarked image is calculated as follows: = ܧܵܯ
1 (ܺ(݅, ݆) − ܺ′(݅, ݆))ଶ ܰݐ ,
(ii) Peak Signal to noise Ratio (PSNR) PSNR is calculated between the original and watermarked image. Larger the PSNR value, more similar is watermarked image to the original image. This image quality metric is defined in decibel as: ܴܲܵܰ = 10 logଵ
(255 × 255) ܧܵܯ
If the PSNR value is greater than 30dB then the perceptual quality is acceptable. 3.2 MATLAB results of different techniquesA. Least Significent BitSubstitution Method:
(a)
(b)
(c)
Figure3. (a) Cover image (b) Watermark image (c) Watermarked image (PSNR = 50.76).
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Vishal Verma and Mrs. Jyotsna Singh B. DCT Method:
(a)
(b)
(c)
Figure4. (a) Cover image (b) Watermark image (c) Watermarked image (PSNR = 33.69). 69).
C. DWT Method:
(a)
(b)
(c)
Figure5. (a) Cover image (b) Watermark image (c) wavelet subbands
(a)
(b)
Figure6. (a) Watermarked image (PSNR = 12.28at SF=0.5) SF=0.5 (b) Extracted Watermark image (PSNR = 13.02 at SF=0.5).
D. SVD Method:
(a)
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(b)
181 Digital Image Watermarking Techniques: A Comparative Study Figure7. (a) Cover image (b) Watermark image
(a)
(b)
Figure8. (a) Watermarked image (PSNR=16.64 at SF=0.5)(b) Extracted Watermark image (PSNR=35.56 at SF=0.5)
E. SVD and DWT Hybrid Method: i.
Method No.1
(a)
(b)
Figure9. (a) Watermarked image (PSNR= PSNR=21.67 at SF=0.5) (b) Extracted Watermark image (PSNR=19.28 19.28 at SF=0.5) SF=0.5
ii.
Method No.2
(a)
(b)
Figure10. (a) Watermarked image (PSNR=25.94 at SF=0.5) (b) Extracted Watermark images (PSNR=21.44, 21.44, 11.10, 12.21 and13.14 at SF=0.5)
iii.
Method No.3 (Our Method)
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Vishal Verma and Mrs. Jyotsna Singh
Figure11. (a) Watermarked image (PSNR=19.42 at SF=0.5)(b) Extracted Watermark image (PSNR=32.14 (PSNR=32.14 at SF=0.5)
F. Comparison Table of Results: Calculation of PSNRs TABLE I.
PERFORMANCE OF LSB SUBSTITUTION METHOD
LSB Substitution Method
PSNR
Watermarked image
50.76
Extracted watermark image
2.846
Table 1 shows the peak signal to noise ratio (PSNR) of watermarked image and watermark image which is extracted from Watermarked image. Itt is very low because the extracted watermark image consist only upper four bit planes besides 8 bit planes. TABLE II.
PERFORMANCE OF DCT METHOD (PSNR)
Coefficient k
10
30
50
70
90
PSNR
42.8
37.35
33.69
31.23
29.39
Table II is representing the values of PSNR for watermarked images for different values of k, where k is a secret key for embedding watermark, called minimum coefficient difference which is difference of two DCT blocks. TABLE III.
PERFORMANCE OF DWT, SVD & SVD+DWT HYBRIDE METHOD (PSNR)
Techniques
Process/SF*
0.1
0.3
0.5
0.7
0.9
Embedding
25.25
16.16
12.28
9.91
8.34
Extracting
31.49
24.01
13.02
9.68
7.25
Embedding
37.68
22.34
16.99
14.49
13.09
Extracting
64.34
37.73
35.5
33.79
32.97
Embedding
32.39
23.89
21.67
20.60
19.96
Extracting
50.56
25.02
19.28
15.92
13.98
Embedding
30.03
26.77
25.94
25.56
25.34
Extracting W1
41.10
28.39
21.44
17.38
14.62
Extracting W2
28.89
15.38
11.10
8.59
6.84
Extracting
32.02
16.90
12.21
9.41
7.39
DWT
SVD
SVD+DWT
Method-1
SVD+DWT
Method-2
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183 Digital Image Watermarking Techniques: A Comparative Study W3
SVD+DWT
Extracting W4
33.01
17.83
13.14
10.63
8.85
Embedding
31.37
23.06
19.42
17.23
15.72
Extracting
56.90
54.42
32.14
24.24
20.39
Method-3
Table III is displaying the PSNR of embedding (Watermarked image) and extracted watermark image with different values of scaling factors.*(SF-Scaling Factor). TABLE IV.
COMPARISON OF OUR HYBRIDE METHOD TO OTHER HYBRI METHODS PSNR AS EXTRACTING WATERMARK
Method\SF
0.1
0.3
0.5
0.7
0.9
Method No.1
50.56
25.02
19.28
15.92
13.98
Method No.2
41.10
28.39
21.44
17.38
14.62
Our Method
56.90
54.42
32.14
24.24
20.39
Table IV representing the PSNR values of recovered image from watermarked. This shows that in our algorithm the PSNR value is improved as compared to rest techniques.
Figure12. Graph of PSNRs of different hybrid methods
IV. CONCLUSION In This paper we have discuss different type of techniques of embedding watermark and as per result shown our proposed method based on SVD DWT hybrid Technique have higher PSNR of Extracted watermark image. For the further work for watermarking in digital image we can embed watermark using new wavelet transform like Lifted wavelets and stationary wavelets, S-transform etc. REFERENCES [1] Ingemar Cox, Matthew Miller, Jeffrey Bloom, Mathew Miller, “Digital Watermarking,” Morgan Kaufmann publication, 2001. [2] R. G. van Schyndel, A. Z. Tirkel, and C. F.Osborne, “A Digital Watermark," in Proc. 1994IEEE Int. Conf. on Image Proc., vol. II, (Austin,TX), pp. 86-90, 1994.
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Vishal Verma and Mrs. Jyotsna Singh [3] Darshana Mistry,”Comparison of Digital Water Marking methods” (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 2905-2909. [4] Yusnita Yusof and Othman O. Khalifa, “Digital Watermarking For Digital Images Using Wavelet Transform”, Proc. IEEE conference, pp. 665-669, March 2007. [5] R. Liu and T. Tan, “An SVD-based watermarking scheme for protecting rightful ownership,” IEEE Trans. Multimedia, vol. 4, no. 1, pp. 121–128, Mar. 2002. [6] Chih-Chin Lai and Cheng-Chih Tsai, “Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition,” IEEE Transaction on instrumentation and Measurement, vol. 59, no. 11, November 2010. [7] E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD domain image watermarking: Embedding data in all frequencies,” in Proc. Workshop Multimedia Security, Magdeburg, Germany, 2004, pp. 166–174. [8] H.-T. Wu and Y.-M. Cheung, “Reversible watermarking by modulation and security enhancement,” IEEE Trans. Instrum. Meas., vol. 59, no. 1, pp. 221–228, Jan. 2010. [9] A. Nikolaidis and I. Pitas, “Asymptotically optimal detection for additive watermarking in the DCT and DWT domains,” IEEE Trans. Image Process., vol. 12, no. 5, pp. 563–571, May 2003. [10] V. Aslantas, L. A. Dog˘an, and S. Ozturk, “DWT-SVD based image watermarking using particle swarm optimizer,” in Proc. IEEE Int. Conf. Multimedia Expo, Hannover, Germany, 2008, pp. 241–244. [11] G. Bhatnagar and B. Raman, “A new robust reference watermarking scheme based on DWT-SVD,” Comput. Standards Interfaces, vol. 31, no. 5, pp. 1002–1013, Sep. 2009. [12] Q. Li, C. Yuan, and Y.-Z. Zhong, “Adaptive DWT-SVD domain image watermarking using human visual model,” in Proc. 9thInt. Conf. Adv. Commun. Technol., Gangwon-Do, South Korea, 2007, pp. 1947–1951. [13] S. Mallat, “The theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 654–693, Jul. 1989. [14] Frank Hartung and Martin Kutter, “Multimedia Watermarking Techniques,” Pro. Of the IEEE, vol. 87, NO. 7, JULY 1999. [15] S.H. Wang and Y.P. Lin, “Wavelet tree quantization for copyright protection watermarking,” IEEE Transcations on Image Processing, vol. 13, No. 2, 2004, pp. 154-165. [16] P. Meerwald and A. Uhl, “A survey ofWavelet-Domain Watermarking Algorithms,” Proceedings of SPIE, Electronic Imaging, Security and Watermarking of Multimedia Contents III, San Jose, CA, USA, vol. 4314, 2001.
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