Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 Key-Selectve Patchwork Method for Audo Watermarkng 1 Ch-Man Pun, 2 Jng-Jng Jang 1, Frst and Correspondng Author,2 Department of Computer and Informaton Scence Unversty of Macau, Macau SAR, Chna cmpun@umac.mo, ma96525@umac.mo do: 10.4156/jdcta.vol4.ssue4.12 Abstract Ths paper presents a Key-Selectve Patchwork Algorthm for audo watermarkng. Two specal subsets of the host sgnal features were selected to embed the watermark sgnal, addng a small constant value to one subset and subtractng the same to another patch. The method s based on wavelet doman and the watermark sgnals were embedded n approxmaton coeffcents. In order to control the naudblty of watermark nserton we try to use varous constant values and select the optmal one to guarantee a desred qualty for the watermarked audo sgnal. The qualty of the watermarked sgnal s evaluated by PSNR (Peak Sgnal Nose Rato) method and Extract Rato(ER) after varous attacks. Smulaton results show that Key-Selectve Patch Method s robust aganst varous common attacks such as flterng, resample and so on. 1. Introducton Keywords: Dscrete wavelet transform, patchwork, dgtal watermarkng Many watermarkng algorthms have been proposed [1-3]. Most of them are produced for mage or vdeo. However, the number of audo watermarkng algorthms s relatvely few [1-3]. There are several reasons for the dffculty of developng audo watermarkng algorthms. Most of all, comparng to other human sensory organs, such lke eyes noses, the human ear s far more senstve. Human ears are so senstve that even a small amount of embedded nose can be detected, especally when the watermark s embedded to the sgnal whose power s weak. It s found that audo watermarkng s a good soluton to address the problems, such as llegal usage of copyrghted audo. When the copyrght volaton has occurred, audo watermarkng can be used as the evdence of copyrght nfrngements and show the owner of the dgtal products. Audo watermark nformaton s a techncal and ntentonal nose. The nformaton such as author, create date, usage methods and other nformaton related to the copyrght can be embedded to the orgnal audo sgnal to dentfy the copyrght of the dgtal products. Moreover, the watermark can possbly be employed to trace the usage of the dgtal products or to lmt the number of copes exsts by nsertng the counter or some specal trackers to the sgnals. In addton, usng the author been embedded n the audo sgnal, rghtful ownershp problem can be addressed. Dgtal watermarkng s the technology that embeds the watermark nformaton nto the orgnal sgnal and generates watermarked sgnal, and make sure that the watermarked sgnal does not mpact normal usage of the sgnal. At least two mportant constrants, whch should be satsfed n each audo watermarkng algorthm, are: robustness and naudblty. Inaudblty s the ablty of the watermarked sgnal to keep audble dstorton free after watermark nformaton nserton. Robustness means that the watermarkng algorthms own the capablty to wthdraw attempts such as modfy or removal of embedded watermarks. In the other word, after varous attacks such as compresson, flterng, nose addton, the more the watermarkng nformaton stll can be detected from the watermarked sgnal, the more robust the watermark algorthm s. However, these two constrants may seem to be contradctory. For example, keepng robustness to attacks n a good level wll lead naudblty of the watermarked sgnal to be at lower level. In addton to these ssues, the other constrants such as securty and complexty of the watermarkng algorthm should also be taken nto consderaton. 117
Key-Selectve Patchwork Method for Audo Watermarkng Ch-Man Pun, Jng-Jng Jang Recently, audo watermarkng technques have acheved notable progress, and several dfferent knds of algorthms for embeddng watermarks nto audo sgnal have been proposed [4]. Makng full use of temporal maskng phenomenon n human audtory systems, Cvej et al. [5] gven a spread spectrum approach for audo watermarkng. Swanson et al. [6] proposed a watermarkng algorthm to embed watermark by drectly modfyng the audo samples. In the way of breakng each audo clp nto smaller peces and addng a perceptually shaped pseudorandom sequence, nserton of the watermark nformaton can be accomplshed. In order to embed more water mark nformaton to the audo sgnal under the two constran of naudblty an robustness, Seok et al. [7] delvered an audo watermarkng method by takng advantage of the human perceptual characterstcs of the audo sgnal to adapts the embeddng strength, so that more watermarkng data can be nserted and more robustness wll mplemented. Grn et al. [8] ntroduced a audo sgnal watermarkng whch use the snusodal model and phases, ampltudes, and dgtal frequences modulatons of the partals. Nma et al. [9] proposed a multplcatve patchwork method for audo watermarkng to stronger watermark nserton wth less audblty. In ths paper, a Key-Selectve Patchwork Algorthm for audo watermarkng s proposed. A revew of the patchwork algorthm s ntroduced n next secton. In secton 3 and 4, the watermark embeddng and extracton algorthms are descrbed n detals. Expermental results are dscussed n secton 5. Fnally, conclusons are drawn n secton 6. 2. Revew of the patchwork Algorthm Patchwork s proposed as a perfect watermarkng algorthm for mages[10]. Bender et al. delvered the man dea of patchwork. The sgnfcant steps n the patchwork embed algorthm are: () usng a gven key to choose two patches randomly, () add a small constant value, d, to the sample values of one patch A and at same tme subtract the same constant value from the sample values of another patch B. Mathematcally speakng s as follow. * * a a d, b b d where a and b represent sample values of the patchwork sets A and B, respectvely. By dong ths, a slght change of the orgnal sample values had occurred. The strength of constant value d wll affect the naudblty of the watermarkng algorthm. In a smlar way, there are two major procedures n the watermarkng detect algorthm. ()choose two patches by usng the same key used n the embed procedure, () calculate the dfference of the sample values between the two patches. Then, compare the expected value of the dfferences of the sample means and the calculate dfference to determne whether the watermark nformaton had been nserted to the orgnal host sgnal or not. Because the dfference of two patches s used to detect the watermark nformaton, t does not need the orgnal sgnal n the retrevng procedure. In ths way a blnd watermarkng algorthm can be acheved. Patchwork tself s a very good algorthm, because t use some mportant mathematcs features to embed the watermark nformaton. Note that * * E[ a b ] E[( a d) ( b d)] E[ a b] 2d where a and b are sample means of the ndvdual sample algorthm assumes E[ a b] 0, so the E[ a b ] 2d. 3. Embeddng the watermarks * * a and b, respectvely. The patchwork The two major steps n the algorthm are: () usng a selectve-key, whch make the dfference between the two patches to be chosen s around zero, to generate two sets of pseudo-random numbers whch wll be used as ndexes to choose two patches, A and B, whose sze s n, n the frequency doman of audo sgnal and () add the small constant value d to the each element of one patch, A, whose elements sum s greater. And subtract the same value from the each element of another patch, B, 118
Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 whose element sum s small. Mathematcally speakng ' ' a a d, b b d. By repeatng step () and step () M tmes, M pars of patches are obtaned. And the 2M sets of pseudo-random numbers, whch are used as ndexes to choose the patches, wll be the watermarkng nformaton n ths proposed algorthm. By dong ths, the orgnal sample values had been slghtly modfed. Accordng to the statstc features, the expected value of the dfferences of the sample values before embeddng s zero. After embeddng, the expected value becomes 2nd. The detect process starts from (1)usng frst two sets of pseudo-random numbers as ndex to choose two patches n the frequency doman of audo sgnal, (2) then calculate the dfference of the two patches. If the dfference s around threshold 2nd, then make the Counter ncreases one. Repeated step(1) and step(2) M tmes. Then compute the value of Extract Rato(ER) = (M-Counter) / M whch s used to decde whether the samples contan watermark nformaton or not. If ER s greater than the decson threshold, then watermarkng s embedded, else the watermarkng s not embedded. The proposed algorthm n ths paper nserts watermarks n the frequency doman. Even though the experment of the proposed algorthm s based on the DWT doman, t can be appled to all frequency domans such as DFT and DCT and so forth. The proposed algorthm n ths paper nserts watermarks n the frequency doman. Even though the experment of the proposed algorthm s based on the DWT doman, t can be appled to all frequency domans such as DFT and DCT and so forth. Embeddng steps are summarzed as follows. 1. Read the orgnal host audo sgnal on whch apply Dscrete Wavelet Transform based on dfferent levels. Next we wll get approxmate coeffcents of the host audo fle F { F1,..., F N }. 2. Use the selectve-key of a random number generator, whch wll guarantee the sum of dfference of the two patches to be more closed to zero. Then generate an ndex set I { I,..., I } 1 2 n 1 whose elements are pseudo-randomly selected nteger values from [ K1, K 2]. 1 K1 K2 N. Splt I nto two parts I 0 { I 1,..., I n } and I 1 { In 1,..., I 2 n}. 3. Defne A { a,..., a } { F,..., F } as the subset of F whose subscrpt corresponds to n I1 I n the element of I 0. Usng the same method, we wll get the smlar defnton for B { b,..., b } { F,..., F }, tll now we had got the TWO patches A and B. 1 n In 1 I2n 4. Calculate the dfference d between 1) f d 0, change a and 2) If d 0, change a and a and b, change the a a d b by equaton a and b base on b b d d :, and respectvely. a a d, andb b d respectvely. b by equaton 5. Apply Inverse Dscrete Wavelet Transform base on the same level as step1 to the modfed approxmate coeffcents and correspondng detal coeffcents to get the watermarked audo sgnals. Selectv-Key Usng the key to generate two ndex sets Orgnal Audo Sgnal DWT Approxmaton Coeffcents Usng the ndex sets to generate two patches Modfy each elements of the patches by addng or subtractng d IDWT Watermarked Audo Sgnal Fgure 1. The flowchart of embed watermark procedure 119
4. Detectng the watermarks Key-Selectve Patchwork Method for Audo Watermarkng Ch-Man Pun, Jng-Jng Jang Detectng the watermark 1. Read the audo sgnals to be detected, on whch apply Dscrete Wavelet Transform based on level three. Next we wll get approxmate coeffcents of the host audo ' ' ' sgnal F { F1,..., F N }. 2. Use the same Selectve-key of a random number generator, whch s used n step2 of watermark embed procedure. Then generate an ndex set I { I1,..., I2 n }, whose elements are nteger values pseudo-randomly selected from [ K1, K 2]. 1 K1 K2 N. Splt I nto two parts I 0 { I 1,..., I n } and I1 { In 1,..., I2n}. ' ' ' 3. Defne A { a,..., a } { F,..., F } as the subset of 1 n I1 I n ' F whose subscrpt corresponds to the element of I 0. Wth the same method, we wll get the smlar defnton ' ' ' ' for B { b,..., b } { F,..., F }, tll now we get the TWO patches A and B '. 1 n In 1 I2n ' ' ' ' 4. Calculate the mean value of dfferences d between A and B, d A B, f the mean value s greater than 2nd, the watermark s detected, otherwse the audo sgnal s not watermarked. Selectve-Key Usng the key to generate two ndex sets Audo Sgnal to be detected DWT Approxmaton Coeffcents Usng the ndex sets to generate two patches Calculate dfference between two patches Decde the watermark embedded or not 5. Expermental Results Fgure 2. The flowchart of detectng watermark procedure To test the robustness of the proposed watermarkng algorthm, many attacks were appled n ths algorthm. All audo sgnals to be tested were 16bts sgned stereo sampled at 44.1kHz. The type of audo samples been tested nclude Rock, Jazz, and Classcal. The sze of each patch was set as n = 1000. And the number of pars of the patches used was set as M = 100. In order to test the naudblty of the algorthm, we attempt dfferent values of d to embed the watermark to the audo sgnal. And the PSNR(Peak Sgnal Nose Rato), whch s used to verfy the naudblty of the watermarked sgnal, was calculated. Through several rounds tests, we found that gven the same d the PNSR value has nothng to do wth whch level of DWT s appled. The PSNR values are the functon of d, and ther relaton s shown as follow. Table 1. Varous PSNR values correspond to dfferent constant values used when embeddng PSNR 62.96 56.94 53.42 50.91 48.98 47.39 42.95 120
Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 Fgure 3. The relaton between d and PSNR In order to nspect the robustness of the algorthm aganst varous types of manpulatons, the followng types of sgnal processng were employed. 1) Down-samplng: The watermarked audo sgnals wth a samplng rate of 44.1kHz were reduced to 22.5kHz Table 2. Extract Rato after down-samplng attack based on varous levels of DWT and constant value d threshold 2 4 6 8 10 12 20 Level1-ER 100% 100% 100% 100% 100% 100% 100% Level2-ER 100% 100% 100% 100% 100% 100% 100% Level3-ER 100% 100% 100% 100% 100% 100% 100% Key-Selectve-Level3-ER 100% 100% 100% 100% 100% 100% 100% 2) Hgh-pass flterng: The cut-off frequences were 800Hz. The second order Butterworth flter was appled. Table 3. Extract Rato after Hgh-pass flterng attack based on varous levels of DWT and constant value threshold 2 4 6 8 10 12 20 Level1-ER 93% 92% 91% 88% 87% 83% 70% Level2-ER 93% 91% 90% 85% 83% 79% 64% Level3-ER 92% 90% 87% 81% 78% 72% 56% Key-Selectve-Level3-ER 94% 93% 92% 89% 87% 83% 81% 121
Key-Selectve Patchwork Method for Audo Watermarkng Ch-Man Pun, Jng-Jng Jang Fgure 4. The relaton between ER and d after Hgh-pass flterng attack, the compare ER between Key-selectve methods used and that dd not used 3) Low-pass flterng: the cut-off frequences were 8000Hz. The second order Butterworth flter was appled. Table 4. Extract Rato after low-pass flterng attack based on varous levels of DWT and constant value d threshold 2 4 6 8 10 12 20 Level1-ER 82% 61% 43% 32% 20% 13% 3% Level2-ER 90% 87% 81% 75% 70% 66% 66% Level3-ER 100% 100% 100% 100% 100% 100% 100% Key-Selectve-Level3-ER 100% 100% 100% 100% 100% 100% 100% Fgure 5. The relaton between ER and d after Low-pass flterng attack, the compare ER between Key-selectve methods used and that dd not used 122
Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 4) Requantzaton: Requantze the audo sgnal from 16bts to 8bts. Table 5. Extract Rato after requantzaton attack based on varous levels of DWT and constant value d threshold 2 4 6 8 10 12 20 Level1-ER 100% 100% 100% 100% 100% 100% 100% Level2-ER 100% 100% 100% 100% 100% 100% 100% Level3-ER 100% 100% 100% 100% 100% 100% 100% Key-SelectveLevel3-ER 100% 100% 100% 100% 100% 100% 100% 5) Mp3 compress: Compress the audo sgnal wth compresson rates of 32, 64, 96kbps. The expermental results are presented n Table 6 and Fgure 8-Fgure 10 Table 6. Extract Rato after mp3 compress attack based on varous levels of DWT and constant value d threshold 2 4 6 8 10 12 20 Mp3-96kbps-ER 95% 95% 96% 97% 100% 100% 100% Mp3-64kbps-ER 86% 87% 87% 89% 90% 90% 91% Mp3-32kbps-ER 73% 74% 76% 76% 77% 78% 78% Key-Selectve-96-ER 95% 96% 97% 98% 100% 100% 100% Key-Selectve-64-ER 89% 90% 91% 91% 92% 93% 94% Key-Selectve-32-ER 78% 79% 81% 82% 82% 83% 84% Fgure 6. The relaton between ER and d after Mp3 attack wth compresson rate 96 kbps based on level3 DWT frequency doman, and the compare ER between Key-selectve methods used and that dd not used 123
Key-Selectve Patchwork Method for Audo Watermarkng Ch-Man Pun, Jng-Jng Jang Fgure 7. The relaton between ER and d after Mp3 attack wth compresson rate 64 kbps based on level3 DWT frequency doman, and the compare ER between Key-selectve methods used and that dd not used Fgure 8. The relaton between ER and d after Mp3 attack wth compresson rate 32 kbps based on level3 DWT frequency doman, the compare ER between Key-selectve methods used and that dd not used The expermental results from Fgure 3 are obvous that the larger the constant value used, the less PSNR values wll get, whch means that wth declne of constant value used for embeddng, the stronger naudblty of the watermarked audo sgnal wll acheved no matter whch level of DWT s appled. We can also easly get the nformaton from Table 2 and Table 5 that watermark s not affected by down-samplng, and requantzaton attacks n all three level of DWT, for the Extract Rato after these two attacks are always one hundred percent. The hgher energy the audo sgnal possesses, the greater mperceptblty tends to be acheved, even f the approxmaton coeffcents are been modfed. It s transparent that the relatvely low frequency 124
Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 components contan more sgnfcant nformaton about the dgtal sgnal, and the relatvely low frequency components seem to be more stable than the hgher one. So f the watermark nformaton s embedded n the relatvely lower frequency feld, the watermarkng algorthm wll be consdered as more robust one, for very lttle data loss occurs at lower frequency felds after varous attacks. So n ths proposed algorthm, the relatvely low frequency components are exploted to embed the watermarkng nformaton. The experment results n the Fgure 4 show that the less level of DWT s appled, more robustness wll be acheved when attacked by Hgh-pass flterng, gven the same embed constant value d. The fgures from Fgure 5 tell that the hgher level of DWT s exploted, the more robustness wll be offered when attacked by Low-pass flterng. From Fgure 6-8, t s evdent that ths proposed algorthm s robust aganst Mp3 compress attack. The hgher compresson rate used, the more robust wll be acheved. From Fgure 4-8, the compare n these fgures showed clearly that after the key-selectve methods used the robustness aganst all knd of attacks had been better mproved. 6. Concluson In ths paper, a selectve-key patchwork method for audo watermarkng has been proposed. Many methods have been appled. Frst, n order to enhance the robustness aganst all knds of attacks, a selectve-key patchwork method was exploted. Second, to guarantee the naudblty of the watermark, we calculate PSNR n ths algorthm. Thrd, by embeddng the watermark base on three dfferent levels of Dscrete Wavelet Transform, we found that t wll be more robust f embed the watermark nformaton base on hgher level of DWT. Fourth, for the relatvely lower frequency components are perceptually sgnfcant, these components are selected to embed the watermarkng to get more robustness feature of dgtal watermarkng. In addton, although the proposed method has been extensvely tested on many dfferent attacks such as flterng, resample and so on, more attacks such as tme shftng, croppng wth half left and so on should be tested and dscussed n the future work. 7. Acknowledgment The authors would lke to thank the referees for ther valuable comments. Ths work was supported n part by the Research Commttee of the Unversty of Macau. 8. References [1] Arnold, M., Audo watermarkng: features, applcatons and algorthms. Multmeda and Expo, 2000. ICME 2000. 2000 IEEE Internatonal Conference on, 2000. 2: p. 1013-1016 vol.2. [2] Hong, J.W.S.a.J.W., Audo watermarkng for copyrght protecton of dgtal audo data. Electron. Lett., 2001. 37: p. 60-61. [3] In-Kwon, Y. and K. Hyoung Joong. Modfed Patchwork Algorthm: a novel audo watermarkng scheme. n Informaton Technology: Codng and Computng, 2001. Proceedngs. Internatonal Conference on. 2001. [4] ProceedngsM. Barn, I.J.C., and T. Kalker, Eds., Dgtal Watermarkng. 4th Internatonal Workshop, IWDW 2005, Sena, Italy, September 15-17, 2005, 2005: p. 260-274. [5] C. Cvej, A.K., and T. Seppanen, Audo watermarkng usng m-sequence and temporal maskng. 7th IEEEWorkshop Applcat. Sgnal Process. Audo Acoust., New York, 2001: p. 227-230. [6] M.D. Swanson, B.Z., A. H. Twefk, and L. Boney, Robust audo watermarkng usng perceptual maskng. Sgnal Process., 1998. 66: p. 337-355. [7] J. Seok, J.H., and J. Km, A novel audo watermarkng algorthm for copyrght protecton of dgtal audo. ETRI J., 2002. 24: p. 181-189. [8] Marchand, L.G.a.S., Watermarkng of speech sgnals usng the snusodal model and frequency modulaton of the partals. IEEE Int. Conf. Acoust., Speech, Sgnal Process, 2004: p. 633-636. [9] Ahad, N.K.K.a.S.M., Robust Multplcatve Patchwork Method for Audo Watermarkng,. IEEE Trans. Audo,Speech and Language Process, 2009. 17(6): p. 1133-1141. 125
Key-Selectve Patchwork Method for Audo Watermarkng Ch-Man Pun, Jng-Jng Jang [10] W. Bender, D.G., N. Mormoto, and A. Lu, Technques for data hdng. IBM Syst. J., 1996. 35(3/4): p. 313-336. 126