The 22nd Iranian Conference on Electrical Engineering (ICEE 2014), May 20-22, 2014, Shahid Beheshti University
Spread-Spectrum Robust Image Watermarking for Ownership Protection A. Ansari, H. Danyali, M.S. Helfroush Shiraz University of Technology
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Abstract— In this paper a new robust, spread-spectrum image watermarking approach is proposed. A two level wavelet transform is applied to the host image. Then three high frequency sub-bands in level 2 are selected and the watermark is embedded by an spread-spectrum technique. Selected sub-bands are divided into square blocks. Each block carries Nb bits of watermark information. Watermark data is added as uncorrelated pseudo-random sequences. In extraction process the same pseudo-random sequence is generated by the same seed. Experimental results approve robustness of the proposed method against JPEG compression, Gaussian noise and median filtering. The proposed method can be used in ownership protection specially where JPEG compression and Gaussian noise attacks occur. Keywords- Digital watermarking, wavelet, spread-spectrum, JPEG compression, noise, median filtering, ownership protection
I.
INTRODUCTION
Rapid growth of information technology and communication systems has provided an opportunity to transfer, share and create data easily. On the other hand, as information technology develops, simultaneously serious concerns rise up. Illegal copying, modifying and tampering data by unauthorized people has challenged internet security. Researchers are aware of this menace and watermarking techniques are widely used in order to protect ownership privileges, authentication and fingerprinting[1]. In watermarking process an image called logo is embedded in another image( often with larger size) titled as host image. It is desired that the logo gets concealed in the host image such that changes to image are imperceptible to viewer but authorized experts can extract the logo from the watermarked image [2]. A watermarking process is called blind if process of watermark extraction doesn’t require the host image[3]. Ideal watermarking method is supposed to degrade image quality as low as possible and at the same time to have great robustness against common image processing attacks such as Gaussian noise and JPEG compression[4]. Also watermark should be embedded in a way that unauthorized people can’t remove it[5]. Watermarking can be divided into various categories in various ways. From view-point of embedding domain, it can be done either in spatial domain or frequency domain[6, 7]. Frequency domain methods are said to have more robustness
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than spatial ones[8]. This is mainly because when image is inverse transformed, watermark is distributed irregularly over the image, making the attacker difficult to read or modify. Due to the fact of localization in both spatial and frequency domain, wavelet transform is the most preferable transform among all other transforms[9]. In [10] the authors proposed a watermarking technique in which the watermark is a gray-scale logo, instead of a binary one. Both host image and watermark are transferred into wavelet domain. Watermark bits are added to significant coefficients of each sub-band of wavelet decomposed image. In order to improve performance of their method, They also gain human visual system(HVS) characteristics to select subbands to embed watermark which resulted in more imperceptibly. Dawei et al. [11] used the wavelet transform locally, based on the chaotic logistic map. This technique shows very good robustness to geometric attacks but it is sensitive to common attacks like filtering and sharpening. Spread-spectrum watermarking provides more robustness and it would be hard to remove a watermark embedded in this way[12]. In spread-spectrum communications, a narrow-band signal is transmitted over a much larger bandwidth such that the signal energy presented in any single frequency is undetectable[12, 13]. A watermark is spread over many frequency bins so that the energy in one bin is very small and certainly undetectable. Because the watermark verification process knows the location and content of the watermark, it is possible to concentrate these weak signals into a single output with high SNR[14]. Destroying such a watermark would require noise of high amplitude to be added to all frequency bins[15]. The location of the watermark is not obvious to a person who intends damaging the image or removing watermark. To be confident of eliminating a watermark, an attack must deteriorate all possible frequency bins with modifications of definite strength which leads to awful visual defects on image. Langelaar et al. [16] proposed a method to embed watermark as a pseudo-random sequence into middle frequencies of the image. In [12] Cox et al. proposed the idea of using spread-spectrum communications for embedding watermark in DCT domain.
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Figure 1: Embedding Process separately, they are grouped into groups of Nb bits and for each group of watermark bits a pseudo-random sequence is Hartung et al. [13] performed a comprehensive study on generated. Therefore Nseq= 2Nb pseudo-random sequences is robustness of spread-spectrum watermarking against different required. attacks and offered suggestions to improve robustness of watermarking. A unique pseudo-random sequence is assigned to each possible permutation of watermark bits. The pseudo-random In [17] a watermarking scheme is proposed using spreadsequence consists of an n×n matrix with ±1 elements. It is spectrum watermarking on DCT domain. They use higher desired to organize the pseudo-random sequences such that frequency ranges of 1st level DCT. Their method has a great cross-correlation between them be as low as possible, i.e. the robustness against median filtering and JPEG compression , covariance matrix of the pseudo-random sequences should be but it provides low capacity for embedding data. Utilized logo as similar as possible to identity matrix. High cross-correlation has dimensions of 8×8 pixels. between sequences increases the risk of error in extraction process. In this paper we propose a wavelet based method using spread spectrum techniques. Wavelet coefficients are divided Using a pseudo-random logo warranties that algorithm is into non-overlapping blocks and each block carries number of not biased to any specific logo and will work for any arbitrary watermark bits. In comparison to [17], we have achieved watermark. The bits of selected watermark are selected randomly, with approximately equal amount of 0’s and 1’s. spread spectrum watermarking with same quality but much higher capacity. The remainder of this paper is organized as Block size has an important role in capacity of embedded follows. Embedding and extraction process are discussed in bits in each block. Selecting bigger size for blocks to embed section II while Experimental results and validation are data increases robustness of the method, but it has a negative conducted in section III. The conclusions are given in section impact on quality of the watermarked image. That is due to the IV. fact that enlarging blocks, causes more wavelet coefficients (or equivalently image information) to be changed. On the other II. THE PROPOSED METHOD hand, embedding more watermark bits in each block provides more capacity but it also leads to rise up error probability in A. Embedding Algorithm watermark detection. So that a trade-off should be held Fig. 1 shows schematics of the proposed embedding between quality, robustness and capacity. algorithm. Watermark is a binary image. The host image is first Following equation is used to embed watermark transferred into wavelet domain which results in four subinformation : bands: LL1,LH1,HL1,HH1. LL1 sub-band contains majority of image information and embedding watermark in this sub-band Iw(u,v)= I(u,v)+ k*seqi (1) provides more robustness. On the other hand, modifying Where I(u,v) denotes an n×n block of wavelet coefficients, coefficients of this sub-band causes drastic fall in image k is the watermark strength, seqi is the i’th assigned sequence quality. To avoid such defect, another wavelet transform is to bits permutation and Iw(u,v) is the wavelet block watermark applied on LL1 sub-band and LH2 ,HL2 , HH2 sub-bands are information. selected for embedding. As shown in Fig. 1 the coefficients of these sub-bands are divided into non-overlapping n×n blocks. Each pseudo-random sequence has length of n2. The value Each block carries Nb bits of watermark information( just as of k should be selected such that a compromise is held between QPSK1 and QAM2 modulation where each symbol represents quality and robustness. Watermark strength and block size definite number of bits ). Instead of embedding watermark bits should not be confused. The same watermark strength can be applied to different block sizes. The best size for block and 1 Quadrature Phase-Shift Keying 2 Quadrature Amplitude Modulation
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appropriate value of watermark strength are obtained after some trials.
error of BER=0.044. Fig. 4 shows the embedded and extracted watermark.
Then Two-level inverse wavelet transform is applied to get watermarked image.
Fig. 5 shows the relation between watermark strength and PSNR for test images. As expected, for greater values of watermark strength, less values of PSNR are obtained. It is obvious from Fig. 5 that PSNR values are affected quite in a similar way for constant watermark strength. The PSNR curves are identical for all test images which is due to adding Gaussian noise with identical power to all images. Table 1 shows the relation between BER and watermark strength for watermarked images.
The embedding algorithm can be summarized as : 1) Apply two-level wavelet decomposition to the host image 2) Divide LH2,HL2,HH2 sub-bands into non-overlapping n×n blocks 3) Pick up Nb bits of watermark and select the corresponding pseudo-random sequence 4) Use Eq. 1 to embed watermark bits. 5) If there is watermark information not embedded still, go back to step 3. 6) Get watermarked image by applying two-level inverse wavelet transform. B. Extraction Algorithm To extract watermark, the same pseudo-random sequences used for embedding process, is reconstructed. To extract watermark from a watermarked image, first two levels of wavelet transform is applied on the watermarked image. Then LH2,HL2,HH2 are divided into n×n blocks as it has been performed during the embedding process. Correlation of each block with all pseudo-random sequences is computed. The sequence with greater correlation is regarded as embedded sequence. So that the bits corresponding to this sequence will form the recovered watermark. The extraction algorithm can be summarized as :
Figure 2 : Original image before watermarking
1) Perform steps 1 and 2 of embedding algorithm 2) Select an n×n block and calculate its correlation with all pseudo-random sequences. 3) Consider sequence with higher correlation as embedded sequence and form the extracted watermark bits in accordance to the sequence. 4) If number of extracted bits is smaller than the whole number of watermark bits, return to step 2. III.
EXPERIMENTAL RESULTS
Performance of the proposed scheme was verified by Matlab platform. Experiments were performed on images titled as : Lena, Mandrill and Goldhill. The algorithm performance is examined in terms of PSNR and BER where BER stand for bit error rate and is equal to number of watermark bits detected wrongly divided into whole number of watermark bits. The Simulation was done for n=4 and Nb=4. In other words wavelet coefficients were divided into 4×4 blocks, each block carrying 4 bits of watermark. A 32×32 pixel binary watermark was embedded in the image. Original and watermarked Lena with watermark strength of 16 are shown in Fig. 2 and Fig. 3 respectively. For watermark strength of 16, PSNR is kept higher than 42 dB and watermark is extracted with a negligible
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Figure 3: Watermarked Lena with watermark strength of k=16 and PSNR= 42.11dB
Figure 4: a) Embedded logo b) Extracted logo The robustness of the proposed method is verified against image processing attacks including median filtering, Gaussian noise and JPEG compression. The standard deviation of added Gaussian noise is 15 and quality factor of JPEG compression is 20%. BER values are listed in Table 2.It can be deduced from table 2 that the proposed method achieves better results for images with less high frequency information. E.g. Mandrill image has more high frequency than Lena and embedding watermark in high frequency sub-bands of Mandrill image results in severer error. In [17] the authors used two-level DCT-based watermarking to embed a 8*8 logo. As they have exploited spread-spectrum technique, the proposed method is compared with method of [17]. Tabel 3 shows results of comparison between the proposed method and[17]. BER and PSNR show the values for both method after recovering watermarked image which is not exposed to any attack. BER(Noise) shows the BER of recovered watermark for watermarked image contaminated to Gaussian noise with standard deviation of 15. Both values are quite similar while the proposed method provides 16 times more capacity. BER(JPEG) represents the BER of recovered watermark for watermarked image compressed by JPEG standard with quality factor of 20%. At the first test, a 8×8 logo and in the second set of experiments a 32×32 logo is embedded by the proposed method. As Table 2 signifies, the proposed method is superior to [17] if an 8×8 logo is exploited. Specifically in terms of PSNR, an improvement of approximately 11.2dB is achieved. All the values of Table 2 are obtained for Lena image and watermark strength of 16 for the proposed method .
Fig. 5: The relation between PSNR and watermark strength Table 1: The relation between BER and watermark strength
Image k=8 k=16 k=20 k=24 Lena 0.097 0.044 0.034 0.024 Mandrill 0.346 0.240 0.190 0.150 Gooldhill 0.275 0.123 0.078 0.056 Table 2: Robustness of the proposed methods against different attacks(k=16) Gaussian Image Median JPEG Noise Filtering Compression
Lena Mandrill Goldhill
0.084 0.303 0.219
0.077 0.257 0.170
0.087 0.265 0.183
Table 3: Comparison of the proposed method with [17]
The Proposed method test 1 The Proposed method test 2 Method of [17]
Logo PSNR BER BER size (dB) (Noise) 32*32 42.11 0.044 0.077
BER (JPEG) 0.087
8*8
54.15
0
0
0
8*8
42.95
0
0.063
0
IV.
CONCLUSION
In this paper an spreead spectrum watermarking algorithm in wavelet domain is proposed. Coefficients of candidate sabbands are divided into n×n non-overlapping blocks. Nb bits of
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watermark information would be embedded in each block. In the extraction process, correlation of embedded sequence with all possible permuatations of sequences is computed. The sequence with higher correlation is supposed to be the embedded sequence. While a 8×8 watermark is used in [17] a 32×32 watermark is embedded in the proposed method and achieved high robustness. Simultaneously BER values are compatible to method of [17] for similar capacity. By setting watermark strength to 16 a trade-off is held between quality, capacity and robustness. It is remarkable that setting watermark strength 20 caused PSNR to be about 40dB for test images. The performance of the proposed method against common image processing attacks are validatd by experimental simulations. REFERENCES [1]
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