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A Digital Watermarking Algorithm Based on Wavelet Packet Transform and BP
Neural Network
Qiao Baoming, Zhang Pulin, Kang Qiao
College of Science, Xian University of Science and Technology, Xian 710054
e-mail: qiaobm@xust.edu.cn
AbstractA new blind digital watermarking algorithm based
on wavelet packet transform and BP Neural Network was
proposed in this paper. The algorithm first decomposed the
host image via discrete wavelet packet transform, then
embedded water-marking into the position which was chosen
by the key in wavelet image. In the process of embedding
watermarking, eight neighborhood pixels around the
embedded point was selected as sample input of the BP neural
network, and the pixel value of the embedded point was chosen
as the ideal output, finally, trained the neural network. Usingthe trained neural network, blind watermarking extraction
was achieved.
Keywords-component; formatting; style; styling; insert
I. INTRODUCTIONDigital watermarking is an important branch of the
information hiding technology. The so-called digitalwatermarking is the digital signal which is not perceived tobe embedded in digital carrier. It can be images, text,symbols, figures, and all can be used as identificationinformation. A large number of perception redundancy partare common in multimedia data, so watermarking embed-
ding in them can achieve the purpose of hiding.The basic idea that wavelet transform apply to image
processing is decomposed the image by multi-resolution,then generate sub-image with different spaces andindependent bands, at last, process the coefficients of sub-image. The neural network has the characteristics ofapproaching non-linear mapping, and has good gener-alization ability. If a neural network was trained in thewatermark embedding process, through studying, thecorresponding relations of pixel values between before andafter embedding the watermark signal is obtained, then thewatermark can be extracted by using the trained neuralnetwork, thus the blind detection of the watermarkingalgorithm is achieved.
This paper presents a new algorithm of digital water-marking based on wavelet analysis and neural network . Thewatermark was embedded in the wavelet image which wasthe host image after discrete wavelet packet transform, sothat the watermarking could be embedded into a narrowerand finer band, and the transparency and robustness ofwatermarking could be enhanced better. This algorithmdecomposed the host image by discrete wavelet packettransform. Then watermarking was embedded into theposition which was chosen by the key in wavelet image. Inthe watermarking embedding process, eight neighborhood
pixels around the embedded point was selected as sampleinput of the BP neural network, and the pixel value of theembedded point was chosen as the ideal output, finally,trained the neural network. Using the trained neuralnetwork, blind watermarking extraction was achieved [1]-[11].
II. WATERMARKING EMBEDDING ALGORITHMA. The generation of waterarking sequences
In the paper, a simple and effective method is adoptedwhich is through the rearrangement of the binary watermarkby columns, one-dimensional watermark sequence W, thesize of K was obtained.
B. Doing wavelet packet teansform for the original imageFirstly, we did level 1 discrete wavelet transform for the
original host image I, and then can get the approximatecomponent CA, the level of detail component CH, thevertical detail component CV, and the detail of the angularcomponent CD. This four sub-images formed Fig.1(b),Secondly, we performed level 1 discrete wavelet transformfor the four components respectively. For example, After
transforming CA, we obtained the approximate componentCAA, the level of detail component CAH, the vertical detailcomponent CAV, and the detail of angular component CAD.After transforming CH, the approximate component CHA,the level of detail component CHH, the vertical detailcomponent CHV, and the detail of angular component CHDcan be get; After transforming CV, the approximatecomponent CVA, the level of detail component CVH, thevertical detail component CVV, and the detail of angularcomponent CVD obtained, and after transforming CD, wehad the approximate component CDA, the level of detailcomponent CDH, the vertical detail component CDV, andthe angular detail component CDD, The sixteen sub-imagesformed Fig.1(c), and Fig.1(a) is the original host image.
a (b) (c)
Fig.1 the flow chart of wavelet packet decomposition
2011 Seventh International Conference on Computational Intelligence and Security
978-0-7695-4584-4/11 $26.00 2011 IEEE
DOI 10.1109/CIS.2011.117
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C. The position selection of embedding watermarkThe wavelet packet transform domain was chosen to
embed watermark. The method to select a position ofembedding watermark was as following. Firstly, divided theimage of wavelet packet coefficients into 33 blocks,
Secondly calculate the standard deviation of each blocks,and arrange the standard deviation in ascending order. Then
choose the block center, which the first K standard devia-tion corresponding to, as an embedding position, where Kindicated the size of the watermark sequence. The positionof embedding watermark must be known first when extractthe watermark, so It is necessary to save the position matrixas a file by using the save command in MATLAB.
The average formula for each block is
++== =
1
1
1
1
),(9
1
m n
njmismean (1)
The formula of computing variance for each block is
++== =
1
1
1
1
2)),((
8
1
m n
meannjmisVar (2)
The formula of standard deviation for each block isvar=stadev (3)
),( mjmis ++ represent the wavelet coefficients of each
block; ),( jis represent the wavelet packet coefficients of
the center position in each block. Variable; nm, is the
variation that other coefficients relative to the centerposition.
D. The establishment of the BP neural network model forwatermarking
The embedded technique of transform domain was usedin this paper. And in order to improve the robustness, neuralnetwork was introduced, since it can fully approximate any
complicated non-linear relationship. Thus the neural net-work model established can well describe the relationshipbetween selected wavelet packet coefficients and theirneighborhood. Neural network model is as following.
1,Si j
1,Si j
1,Si j +
, 1Si j ,Si j , 1Si j+
1,Si j+
1,Si j+
1,Si j+ +
Figue2. 33 Coefficient blocks of wavelet packet
Figure.2 is a 33 block of wavelet packet coefficients,
the center was where the watermark is to be embedded. Weselect eight coefficients of wavelet packet around the centeras the input of BP neural network, and the coefficient ofcenter wavelet packet as the expected output, then train theneural network. Fig.3 was the structure of BP neuralnetwork.
For the multi-layer network, the number of input nodesand output nodes are determined by the problem itself.Choosing the size of network is mainly determining the size
of nodes in hidden layer. The selection of nodes in hiddenlayer is very important for network training and studying. Ifhidden nodes is too few, the network would not have thenecessary ability to learn and necessary information pr-ocessing capabilities.
Fig.3 BP neural network
Conversely, too many hidden nodes increased the
complexity of network structure greatly, and was easier fornetwork to fall into local minimum in the learning process.Meanwhile, It made the network learn very slowly. Thecommon method of selecting is the trail and error method,generally based on experience to select the hidden layer ofnodes, was very random. The empirical formula fordetermining the number of hidden nodes is
dcnnmN ++= (4)
Where Nis the hidden nodes; n is the number of input
nodes; m is the number of output nodes; dc, is the
parameters to be determined. In general, the followingposterior formula is used.
9298.06799.1 ++= nnmN (5)
In this article, 7.4,1,8 == Nmn ,taking 5=N ,The
neural network has three layers, there were 8 nodes in inputlayer,5 nodes in hidden layer, 1 node in output layer. Incoefficient image of wavelet packet S, the coefficient ofwavelet packet which was selected to embed the water-
marking was ijS , and the eight wavelet packet around it was
as the input of network. As shown above, the output of
neural nodes in output layer was ijy , the expected output of
net-work was ijS . We select the embedding positions of K
by the key, and these points and the points around themformed a learning sample space of the network. Thenetwork which is well trained is used for the blind detection
of watermarking.
E. Embed watermarkingEmbed watermarking using the formula as following
)1)(2)(2(),(),( ++= kwjisjip (6)
),( jip was the wavelet packet coefficient of embedded
watermarking, ),( jis was the k-th wavelet packet coeffici-
ents to be embedded according to the key; was the radio
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of the standard deviation of the coefficient block and themaximum standard deviation of all the coefficient blocks;
is constant, taking 02.0= ; was adaptive embedding
intensity, is fixed embedding strength; )(kw represent the
k-th position of watermarking.
F. the Image embedded watermarkingAfter embedding the watermarking, the image
embedded watermarking is obtained by taking inversewavelet transform.
III. WATERMARKING EXTRACTING ALGORITHM The algorithm firstly decomposed the image of em-
bedded watermarking by discrete wavelet packet.
Then determine the embedding position by using thekey, according to that, the input of BP network isdetermined. The simulation result of wavelet packetcoefficient is obtained by the trained BP networksimulation.
After the step in front, extracted the watermarkingusing the formula as following
>
=
)()(,0
)()(,1)('
11
11
kykT
kykTkw (7)
Where Kk 1= )(1 kT was the wavelet coe-
fficient of the k-th embedded watermarking after
discrete wavelet packet transform. )(1 ky was the
coefficient of wavelet packet before embedding intothe position where was simulated by BP neural
network; )(' kw was the value of the k-th extracted
watermark.
At the last, rearranged the extracted watermarkingsequence according to the key, binary watermarking
image was obtained.
IV. THE IMPLEMENTATION OF ALGORITHM
Fig.4 Original host image(I)
In the algorithm, the watermarking image w, the originalhost image I and the image embedded watermark wx arerespectively Figure.4, Figure.6 and Figure.8, the differencebetween the host image and the image embedded watermarkwas invisible, and It was successful for the transparency ofthe algorithm. Fig.5 was wavelet packet coefficient imageafter the decomposition of the original host image, Fig.7
was the wavelet coefficient graph after embeddingwatermark, Fig.9 was the training figure of BP neuralnetwork. The extracted watermark w2, shown in Fig.10, wasthe same visual effect as the image before embedding.
Fig.5 Coefficient image of wavelet packet (ss)
Fig.6 Original watermark image (w)
Fig.7 Coefficient image of wavelet packet embedded watermarking (p)
The experimental results indicated that there were novisual differences between host image and the image em-bedded watermark. And the watermarking had goodimperceptibility. The peak signal to noise ratio
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was 2035.37PSNR = , the extracted watermark and the
original watermarking similarity was 1NC = .
Fig.8 Image embedded watermarking (wx)
Fig.9 Training figure of watermarking image
Fig.10 Extracted watermark (w2)
REFERENCES
[1].G.Voyatzis, I.Pitas, The Use of Watermarks in theProtection of Digital Multimedia Product, Proceedings
of the IEEE.1999,87(7):1197-1207.
[2].John M.Acken, How watermarking Adds Value toDigital Content, Communication of the ACM , 1998,41(7):74-77.
[3].L.F.Turner, Digital data security system, Patent IPNW089/08915,1989.
[4].Van Schyndel R.G, ,Tirkel A.Z, Osborne C.F, A digitalwatermark, International Conference on Image
Processing, 1994,(2):86-90.
[5].Walter R.Bender, Daniel Gruhl,Norishige Morimoto,Techniques for data hiding, Masssachusetts Institute of
Techology Media Lab,1994.
[6].Brassil J.T, Low S; Maxemchuk N.F, Lucent Technol,Bell Labs, Murray Hill NJ, Copyright Protection for the
Electronic Distribution of Text Documents,
Proceedings of the IEEE,July 1999,87(7):1181-1196.
[7].Dong Zheng, Jiying Zhao, RST Invariant Digital ImageWatermarking:Importance of Phase Information, TheIEEE Canadian Conference on Electrical and Computer
Engineering(CCECE)2003.
[8].Shizhong Liu, Bovik A.C, Efficient DCT-domain blindmeasurement and reduction of blocking artifacts, IEEE
Trans on Circuits and Systems for Videa Technolo-
gy,2002,12(12):1139-1149.
[9].Deepa Kundur, Dimitrios Hatzinakos, A Robust DigitalImage Watermarking Method Using Waterlet-Based
Fusion, Proc, of ICIP 97,1997,1:544-547.
[10]. Davis K.J, Najarian K, .Maximizing strength ofdigital watermarks using neural networks, Proceedings
of the International Joint Conference on NeuralNetwoks,2001,4:2893-2898.
[11]. Justin Picard, Arnaud Robert, On the public keywatermarking issue Proceedings of SPIE, 2001,
4314:290-299.
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