COMPARISONS OF DCT-BASED AND DWT-BASED WATERMARKING TECHNIQUES

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1 COMPARISONS OF DCT-BASED AND DWT-BASED WATERMARKING TECHNIQUES H. I. Saleh 1, M. E. Elhadedy 2, M. A. Ashour 1, M. A. Aboelsaud 3 1 Radiation Engineering Dept., NCRRT, AEA, Egypt. 2 Reactor Dept., NRC, AEA, Egypt. 3 Faculty of Engineering, Mansoura University, Egypt. h_i_saleh@hotmail.com, elhadedy2003@yahoo.com, ma_ashour@hotmail.com, mohyldin@yahoo.com ABSTRACT Image watermarking has wide applications in copyright protection, medical safety, fingerprinting, copy protection, broadcast monitoring, data authentication, indexing, data hiding...etc. Different techniques are used in insertion and detection of watermark in both spatial and frequency domains. Frequency domain requires transforms such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). A comparison between DWT-based watermark insertion and detection techniques and DCT-based techniques is presented. By using several attacks such as compression, blurring, add noise, intensity adjustment, gamma correction, rotation, and rescale, the robustness of the compared techniques are measured and compared. INTRODUCTION Multimedia can be defined as multiple forms of media (text, graphics, images, animation, audio, and video) that are used together in the same application. Protection of multimedia content has recently become an important issue because of the transition from analog to digital technologies. There are several proposed or actual watermarking applications [1], such as broadcast monitoring, owner identification, proof of ownership, transaction tracking, content authentication, copy control, and device control. Important properties of an image watermarking system include perceptual transparency, robustness, and high data capacity. Robustness is the resistance of the watermark against intentional attacks such as addition of noise, filtering, lossy compression, scaling etc. Data capacity refers to the amount of data that can be embedded without affecting perceptual transparency. The relative importance of these properties depends on the requirements of a given application. Watermarks and watermarking techniques can be divided into various categories in various ways. The watermarks can be applied in spatial domain, and alternatively in frequency domain [2], [3]. It has been pointed out that the frequency domain methods are more robust than the spatial domain techniques. Frequency domain techniques incorporate the watermark into transforms includes DCT and DWT. Different types of watermarks according to different classifications are shown in Fig. 1 [1]. Fig. 1. Types of watermarking techniques. Watermarking techniques in DCT domain allow an image to break up into different frequency bands, making it much easier to embed watermarking information into the appropriate frequency bands of an image. The low frequency band carries the most important visual parts of the image. On the other hand, the high frequency band is exposed to removal through compression and noise attacks. The middle frequency bands avoid the removal through compression as well as don t contain important visual information [4]. Therefore, the middle frequency bands are the suitable region of watermark insertion. The DWT (Discrete Wavelet Transform) separates an image to a lower resolution approximation image (LL) as well as horizontal (HL), vertical (LH) and diagonal (HH) detail components. One of the many advantages of the DWT is being more accurately model aspects of the HVS as compared to the FFT or DCT.

2 This allows us to use higher energy watermarks in regions that the HVS is known to be less sensitive to, such as the highresolution detail bands {LH, HL, HH}. Embedding watermarks in these regions allow us to increase the robustness of our watermark, at little to no additional impact on image quality [4]. The aim of this paper is comparing between DCT Domain Watermarking techniques and Discrete Wavelet Transform Watermarking algorithms. WATERMARKING METHODS 1-DCT Domain methods a) Mid-band Coefficient Exchange One of the more common techniques utilizes the comparison of middle-band DCT coefficients to encode a single bit into a DCT block [5]. To begin, we define the middleband frequencies (F M ) of an 8x8 DCT block. F L is used to denote the lowest frequency components of the block, while F H is used to denote the higher frequency components. F M is chosen as the embedding region as to provide additional resistance to lossy compression techniques, while avoiding significant modification of the cover image. Next two locations B i (u 1, v1) and B i (u 2, v2 ) are chosen from the F M region for comparison. Rather then arbitrarily choosing these locations, extra robustness to compression can be achieved if we base the choice of coefficients on the recommended JPEG quantization table shown below in Table (1). If two locations are chosen such that they have identical quantization values, we can feel confident that any scaling of one coefficient will scale the other by the same factor preserving their relative size. Table (1). Quantization Values used in JPEG compression scheme. Based on the table, we can observe that coefficients (5, 2) and (4, 3) or (1, 2) and (3, 0) would make suitable candidates for comparison, as their quantization values are equal. The DCT block will encode a 1 if B i (u 1,v 1 ) > B i (u 2,v 2 ); otherwise it will encode a 0. The coefficients are then swapped if the relative size of each coefficient does not agree with the bit that is to be encoded. The robustness of the watermark can be improved by introducing a watermark strength constant k, such that B i (u 1, v1) - B i (u 2, v2) > k. Coefficients that do not meet this criterion are modified though the use of random noise as to then satisfy the relation. Increasing k thus reduces the chance of detection errors at the expense of additional image degradation [5]. Calculate the Similarity ratio to calculate range of robustness after attacks. b) Comparison-Based Correlation in DCT midband Another technique for embedding [4] is to exploit the correlation properties of additive pseudo random noise patterns as applied to image after calculates DCT coefficients as blocks 8X8 and select FM band. A pseudo random noise (PN) pattern W(x,y) is added to the cover image I(x,y) according to the equation IW(x,y)=I(x,y)+k*W(x,y). Where k denotes the gain factor and IW is the resultant watermarked image. As k increases the robustness of the watermark increases but perceptual quality of the image decreases. To retrieve the watermark, the same PN generator is seeded with the key and the correlation between the noise pattern and the image computed. If the correlation exceeds certain a threshold T, the watermark is said to be detected [4]. Calculate the Similarity ratio to calculate range of robustness after attacks. 2- DWT domain Watermarking methods a) DWT based multiple watermarking [6] a-1) Watermark Embedding The original image I represented as I = ( x( i,,0 i, j M ), (1) Where x(i, is intensity pixel and M represents the size of the image. Binary digital watermark J and K are represented as J, K = ( x( i,, 0 i, j M/4), x( i, {01, }. (2) The two level decomposition of the cover image are performed and then embed the watermarks into the second level LL and HH

3 band respectively. The following relationship can form the DWT of the watermarked image: V ' = V + βw ( i,, (3) V ) th ' = ( i, j watermarked DWT coefficient V ) th = ( i, j DWT coefficient of value V. β is scaling factor determining the strength of the watermark which is 0.1 in [7]. The inverse DWT generates a watermarked image. a-2) Watermark Extraction Algorithm The forward two level decomposition of the suspected and original watermarked image is performed to recover the LL and HH bands. Subtraction of the suspected and original bands is performed to recover the watermark bits in both LL and HH bands. Then it is divided by the watermark strength factor β. The operation is summarized as W '( i, = ( V ' i, j Vi, / β (4) Then a quantitative measurement defined as Similarity Ratio (SR) is used to compare embedded and extracted watermarks. SR = S /( S + D), (5) Where S denotes the number of matching pixel values in compared images, and D denotes the number of different pixel values compared images. Finally, select the best band by SR to be the insertion band. The robustness is measured by the SR. b) CDMA Spread Spectrum in Wavelet Domain b-1) In insertion The wavelet transform bands is calculated for the host image then PN code generated for zero state is added to (LH) and (HL) bands in the host image when value of watermark pixel equal zero. Then, inverse wavelet transform is performed. Thus, the watermarked image is similar to the host image because the (LL) band isn't changed after adding the PN code. b-2) In detection (extraction) The wavelet transform bands of watermarked image is calculated, then reset the PN generator by using the same key in insertion operation. The PN code for (LH), (HL) bands is generated. The mean correlation in LH, HL bands is used as a threshold. A pixel in the extracted watermark is equal zero when each coefficient correlation is greater than the threshold [8]. The medical host RESULTS AND DISCUSSION The watermark Fig. 3. Graph of relations between similarity ratios, and compression ratios. 1-Robustness to JPEG compression The output image of each algorithm is compressed using the JPEG algorithm and the JPEG compression ratio is varied from 5 to 100. The medical host image of size 128x128 and watermark image are shown in Fig. 2. The size of watermark 32x32 in DWT algorithms but in DCT algorithms the size of it is 16x16. Fig. 3, and Fig. 4, shows the variance of SR, PSNR (Peak Signal to Noise R atio) with respect to the different compression ratios in DWT algorithms and DCT algorithms. Fig.2. The host image and watermark image.

4 2- Smoothing filtering and Noise Addition Net effect of the low pass filtering is image blurring. Smoothing filters are used for blurring and noise reduction. We have used 3x3 wiener filter for low pass filtering and Average filter 3x3. Gaussian noise with a zero mean and variance σ=0.01, speckle noise with a zero mean and variance and salt & pepper noise with noise density is added to such image for checking the robustness of the method against this attack. The blurring by using Average filter, Wiener filter and noise addition attacks effects are shown in Table (2). Note, the results of the DWT technique in Figure 4, and Table (2) are selected from the higher results of two bands LL and HH. 3- Robustness to Geometrical Transformations Scaling in the spatial domain causes the inverse scaling in the frequency domain. If N x M is the size of the initial image then after scaling with a factor S > 0 size of the scaled image is SN x SM. Here the scaling factor is Fig.4. Graph of relations between PSNR (db), and compression ratios. chosen as 0.5. Scaling attacks effects are shown in Table (2). 4- Gray Scale Manipulations Intensity adjustment is a technique for mapping an image's intensity values to a new range in this test [LOW_IN=0 HIGH_IN=0.8], [LOW_OUT=0 HIGH_OUT=1] and gamma corrections is part of intensity adjustment at gamma factor 1.5. Table (2) shows the intensity adjustment and gamma corrections effects. Table (2). Effects of several attacks on compared algorithms. From Figure 3, the Mid-band Coefficient Exchange Technique [5] is the most robust one (the highest SR) against compression. As well as, the Mid-band Coefficient Exchange Technique has the highest PSNR with respect to others techniques. The PSNR of the other techniques are almost the same for compression ratios greater than 5 as shown in Figure 4. For the other attacks shown in Table (2), the Mid-band Coefficient Exchange Technique has the highest SR than the others indicating that being the most robust technique among the compared techniques. CONCLUSIONS This comparative study of the selected four algorithms concludes that the Mid-band Coefficient Exchange algorithm [5] is the most robust one against the tested attacks. From the viewpoint of perceptiveness, the Mid-band Coefficient Exchange algorithm has PSNR of watermarked image higher than the others except in the gamma correction attacks. Thus, the Mid-band Coefficient Exchange algorithm is the best one among those four compared algorithms in robustness and perceptiveness. REFERENCES [1] Cox, I. J., Miller, M. L. and Bloom, J. A., Digital Watermarking, Morgan Kaufmann Publishers, [2] Langlar, G., Setyawan, I. and Langend, R. L., Watermarking digital image and Video data A state of art Overview, Signal processing Magazine, 20, (2000). [3] Swanson, M. D., Kobayashi, M. and Tewfik, A. H., Multimedia data embedding

5 and watermarking technologies, Proceedings of the IEEE, 86, (6),1064, (1998). [4] Langelaar, G., Setyawan, I., and Lagendik, R., "Watermarking Digital Image and video Data", IEEE Signal processing Magazine, Vol. 17, 20, (2000). [5] Johnson, N., and Katezenbeisser, S., "A Survey of steganographic Techniques ",Eds. Northwood, MA: Artec House, 43, (1999). [6] Mehul, R., and Priti, R. Discrete Wavelet Transform Based Multiple watermarking Scheme, in Proc. IEEE Int. Conf. TENCON 2003, Bangalore, India,(2003). [7] Kundur, D., and Hatzinakos, D., A robust digital image watermarking method using wavelet-based fusion, in Proc. 4th IEEE Int. Conf. Image Processing 97, Santa Barbara, CA, pp. 544, (1997). [8] Inoue, H., Miyzazaki, A. and Katsura, T., "An Image Watermarking Method Based on the Wavelet Transform", International Conference on Image Processing, ICIP 99 Proceedings, 1, pp. 296, (1999).