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An improved Spread Spectrum Watermarking technique to withstand Geometric Deformations
 

An improved Spread Spectrum Watermarking technique to withstand Geometric Deformations

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Here, we propose a new method for the ...

Here, we propose a new method for the
watermarking to withstand the geometric attacks, which
may occur during the transmission of the watermarked
image. The underlying system is based on Direct Sequence
Code Division Multiple Access (DS-CDMA). The algorithm
for the normalization has been formulated for use in black
and white images. The watermark is spread across the
carrier image by using the pseudo-random noise sequences
of optimal period and retrieval is made by the use of
correlation. Private Key technique is used so the
transmission is very secure. Matlab was used to implement
the algorithm discussed here.

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    An improved Spread Spectrum Watermarking technique to withstand Geometric Deformations An improved Spread Spectrum Watermarking technique to withstand Geometric Deformations Document Transcript

    • ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 An improved Spread Spectrum Watermarking technique to withstand Geometric Deformations A. Sangeetha 1 ,K.Anusudha 2 ,B.Gomathy 3 and K.Surya Tej 4 1 asangeetha@vit.ac.in1 2 Kanusudha@vit.ac.in2 3 gomathy_tc16@yahoo.co.in3 4 kunchesuryatej@gmail.com4 School of Electrical Sciences VIT University, Vellore-14Abstract—Here, we propose a new method for the modulated spread spectrum with frequency spectrum,watermarking to withstand the geometric attacks, which centered at the carrier frequency. The information ismay occur during the transmission of the watermarked demodulated at the receiving end by multiplying theimage. The underlying system is based on Direct Sequence signal by a locally generated version of the pseudo-Code Division Multiple Access (DS-CDMA). The algorithmfor the normalization has been formulated for use in black random code sequence. This process, known as "de-and white images. The watermark is spread across the spreading", mathematically constitutes a correlation ofcarrier image by using the pseudo-random noise sequences the transmitted PN sequence with the PN sequence thatof optimal period and retrieval is made by the use of the receiver believes the transmitter is using.correlation. Private Key technique is used so thetransmission is very secure. Matlab was used to implement IV. WATERMARKING METHODOLOGYthe algorithm discussed here. I. INTRODUCTION The original image is taken and converted into gray Geometric deformations include rotation, scaling, scale if required. Normalization procedure is applied totranslation, shearing, random bending, and change of the original image. A PN sequence is generated using aaspect ratio (e.g., [1]–[3]). It is well known that a small key element, which is confidential to the organizationamount of rotation and/or scaling can dramatically alone. Create a two-dimensional (2-D) watermark withdisable the receiver from detecting the watermark [4].A the same size as the normalized image. Binary pseudo-watermark is robust if it cannot be impaired without also random sequences pi, i=1,2,3…. M is generated, asrendering the attacked data useless. Watermark signature patterns using the private key as seed, whereimpairment can be measured by criteria such as miss M is the number of bits in the watermark message.probability, probability of bit error, or channel capacity. Then the last two digits of the sequence will be XORedHence, robustness can be evaluated by simultaneously and the value will be shifted once this process willconsidering watermark impairment and the distortion of continue till code of length equal to the length of thethe attacked data. The key idea of this watermarking cover image is generated. A 1-D DS-CDMAscheme is to use a normalized image for both watermark watermark signature by modulating the watermarkembedding and detection. message with the patterns generated in previous steps is created. Message is embedded to the normalized image. II. WATERMARKING USING CDMA TECHNIQUES Desired watermarking strength is used before The CDMA technique is a spread spectrum technique addition.A mask image is created, which is a binarythat spreads the transmitted signal over a wide image of the same size as the normalized image. Thisfrequency band, which is much wider than the actual image has 1s within the support of the normalizedminimum bandwidth required. This technique ensures image and 0s elsewhere. Using the mask image thethe survival of watermark under various attacks due to boundary is masked of if it is excess than the coverredundancy. image.Inverse normalization is done to this watermark embedded image. This is the watermarked image and III. DIRECT SEQUENCE SPREAD SPECTRUM this is transmitted. In this form of modulation, a pseudo-random noise In the receiver side the image is normalized. Using the generator creates a high-speed pseudo-noise code same key PN sequence is again generated. Correlation sequence (sequence of 1 and −1 values). Direct- is performed between the watermarked image and the sequence spread-spectrum transmissions multiply the PN sequence. Mean of the correlation values are taken data being transmitted by this "noise" signal; thus, it and a threshold is fixed. Message is decoded using this directly sets the transmitted radio frequency (RF) threshold. bandwidth. The result of modulating an RF carrier with such a code sequence is to produce a direct-sequence- 32 © 2010 ACEEE DOI: 01.ijsip.01.01.07
    • ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 V. IMPLEMENTATION So that the resulting image, denoted by, f 3(x, y) = Ay [f 2(x, y)]. γ Can be calculated using the formula, The parameters by which the image is normalized areestimated from the geometric moments of the image[4]. By putting μ11(3) =0 we getA. Image Moments and Affine TransformsLet f (x, y) denote a digital image of size M x N. Itsgeometric moments mpq and μpq central moments, p, q= 0, 1, 2, 3… are defined, respectively as Scale f 4(x, y) in both x and y directions with As = α 0 so that the resulting image denoted by, 0 β And f 4(x,y) = As[f 3(x,y)] achieves 1) A prescribed standard size. 2) μ50(4)>0 and μ05(4)>0. Where Where, α= Standard image size/number of columns in y-sheared image. β=Standard image size/number of rows in y-An image g (x,y) is said to be an affine transform of sheared image.f(x,y) if there is a matrix A= a11 a21 The final image f4 (x, y) is the normalized image. a12 a22and the vector d = d1 such that f(x,y)=g(x,y), d2whereB.Normalization procedureThe four steps of normalization are: Center the image f (x,y); this is achieved by settingthe matrix A= 0 1 and the 1 0Vector with d= d1 with, d2 Let f 1(x, y) denotes the resulting centered image. Apply a shearing transform to f 1(x, y) in the xdirection with matrix Ax = 1 β 0 1So that the resulting image Figure 1. Block diagramdenoted by, f 2(x,y) = Ax[f1(x,y)].β can be calculatedusing the formula, C.Embedding The addition of the PN sequences to the cover imageIn particular, we may have the following two is done according to the equation:scenarios: Iw (x, y) = I (x, y) + k × W (x, y) 1) One of the three roots is real and the other two This is shown in figure given bellow are complex, we select the real root Where, Iw (x, y) denotes the watermarked image. 2) All three roots are real, then we pick the I (x, y) denote the actual cover image. median of the three real roots. W (x, y) denotes a pseudorandom noise pattern that is added to the image. Apply a shearing transform to f 2(x, y) in the y K denotes the gain factor. direction with matrix Ay = 1 0 γ 1 33© 2010 ACEEEDOI: 01.ijsip.01.01.07
    • ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 Figure 2(a) Embedding process step-1 Figure 5(a) watermark message Figure 5(b) watermarked image This image is masked to remove borders in watermark message if greater than normalized image. To the normalized and masked image inverse normalization is Figure 2(b) Embedding process step-2 done. Inverse normalization involves the steps, which is simply the inverse of the steps involved in D.Extraction normalization. The multiplier output C of figure.3 is given by C = Iw (x, y) × b (x, y) = (a(x,y) × b(x,y) + I(x,y)) × b(x,y) = a(x,y) × b^2(x,y) + I(x,y) × b(x,y) Figure 6(a) masked image Figure 6(b) image to be transmitted Figure.3 extraction process Receiver side results for a watermarking strength K= 2 The watermark image a (x, y) is multiplied twice with the noise signal b (x, y) which becomes 1 whereas the unwanted or the cover image I (x, y) is multiplied only once with the noise signal that can be filtered out during the process of correlation by setting the Recovered Watermark threshold as mean of correlation. VI. RESULT ANALYSIS Figure 7(a) received image Figure 7((b)recoverd watermark The first step is normalization. This difference image below shows that the technique ensures high degree of fidelity. As the gain is increased from 2 to 4, the recovery of the watermark improves, but at the cost of distorting the watermarked image.Figure 4(a) original image Figure 4(b) normalized imageThen the watermark is embedded. Figure 8. Difference image 34 © 2010 ACEEE DOI: 01.ijsip.01.01.07
    • ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 VII. ATTACKS A .BER after Geometric Distortion TABLE I. Comparison between Watermark Recovery with and ∗ Flipping without Normalization TABLE II. BER for flipping Flipping BER Type of attack With normalization Without Normalization Horizontal / Vertical 0.0443 Line & column Removal ∗ Scaling TABLE III. BER for scaling Scaling Scaling BER 0.75 0.0461 0.5 0.0461 Aspect ratio 1.1 0.0461 Change 1.5 0.0425 ∗ Aspect ratio change Shearing TABLE IV. BER for change of aspect ratio. Aspect Ratio BER Affine (1, 0.8) 0.0490Transformation (1, 0.9) 0.0437 (1, 1.1) 0.0437 Horizontal (1, 1.2) 0.0514 Flipping ∗ Line and column removal TABLE V. BER for line & column removal Vertical Flipping Number of Rows & Columns BER Median filtering (1, 1) 0.0425 (17, 5) 0.0443The above shows the watermarking recovery with andwithout normalization. From the recovered images it is ∗ Shearingseen that the normalization procedure resulted in a TABLE VI. BER for shearingbetter geometric deformation resistance to the images. Shearing BER VIII. BIT ERROR RATIO Waterm strength Vs BER ark (0, 1%) 0.0319 0.2 (5%, 5%) 0.0461 0.15 ∗ General geometric affine transformation BER 0.1 TABLE VII. BER for general geometric affine transformation 0.05 Matrix BER 0 1 2 3 4 5 6 7 8 9 1.1 0.2 0 Waterm strength ark -0.1 0.9 0 0.1329 0 0 1Figure 9. Plot between watermark strength Vs BER 0.9 -0.2 0 0.1010From the plot we can infer that the Bit Error rate 0.1 1.2 0 0 0 1decreases with the increase in watermark strength. 1.01 0.2 0 0.0691 -0.2 0.8 0 0 0 1 35© 2010 ACEEEDOI: 01.ijsip.01.01.07
    • ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010 IX. CONCLUSIONThe proposed algorithm achieves its robustness by bothembedding and detecting the watermark message in thenormalized images. It is demonstrated that theproposed algorithm can achieve very low decodingBER when used with multi bit watermarks undervarious affine attacks. From the analysis, the gainfactor k=2 is arrived which gives a good balancebetween the visual quality and watermark robustness.The above process provides high security to thecopyright information and preventing access fromunauthorized users. REFERENCES [1] F. A. P. Petitcolas, R. J. Anderson, and M. G. Kuhn, “Attacks on copyright marking systems,” in Proc. Workshop Information Hiding, Portland, OR, Apr. 1998, pp. 15–17. [2] M. Kutter and F. A. P. Petitcolas, “A fair benchmark for image watermarking systems,” presented at the Electronic Imaging, Security and Watermarking of Multimedia Contents, vol. 3657, Sans Jose, CA, Jan. 1999. [3] J. Cox and J. P. M. G. Linnartz, “Public watermarks and resistance to tampering,” presented at the IEEE Int. Conf. Image Processing, vol. 3, 1997. [4] C. Y. Lin, M.Wu, J. A. Bloom, I. J. Cox, M. Miller, and Y. M. Lui, “Rotation, scale, and translation resilient public watermarking for images,” IEEE Trans. Image Process., vol. 10, no. 5, pp. 767–782, May 2001 [5] M. Alghoniemy and A. H. Tewfik, “Geometric distortion correction through image normalization,” presented at the ICME Multimedia Expo, 2000. [6] Ingemar J. Cox, et al., “Secure Spread Spectrum Watermarking for Multimedia”, IEEE Trans. on Image Processing, Vol. 6, No.12, Dec 1997, pp.1673-1687. [7] D. Shen, and Horace H., “Generalized Affine Invariant Image Normalization,” IEEETrans. Pattern Anal. and Machine Intelligence, Vol. 19, No. 5, pp. 431-440, May 1997. 36© 2010 ACEEEDOI: 01.ijsip.01.01.07