IMPLEMENTATION OF QIM BASED AUDIO WATERMARKING USING HYBRID TRANSFORM OF SWT-DCT-SVD METHODS OPTIMIZED WITH GENETIC ALORITHM

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1 IMPLEMENTATION OF QIM BASED AUDIO WATERMARKING USING HYBRID TRANSFORM OF SWT-DCT-SVD METHODS OPTIMIZED WITH GENETIC ALORITHM Ryan Amnullah 1, Gelar Budman 2, Irma Saftr 3 1, 2, 3 FakultasTeknk Elektro, Unverstas Telkom 1 ryanamnullah@students.telkomunversty.ac.d, 2 gelarbudman@telkomunversty.ac.d, 3 rmasaf@telkomunversty.ac.d Abstract Nowadays, almost all data transacton s done through the nternet because t s easy and could be accessed anywhere. The fle s uploaded drectly wthout any securty or scannng makng people able to upload llegal fles or fles that s not owned by them. Ths volaton of copyrght becomes a huge problem as t reduces the owner s proft. That s why watermarkng method s created. Watermarkng s a method of embeddng secret nformaton to a host data. The nformaton could be embedded nto an audo, mage or vdeo data. Ths research wll desgn an audo watermarkng by combnng 3 methods of transformaton: Statonary Wavelet Transform (SWT), Dscrete Cosne Transform (DCT), and Sngular Value Decomposton (SVD). SWT separates data s frequency nto hgh and low. After that, DCT maps correlated hgh frequency data nto uncorrelated coeffcent. Then those coeffcents wll be deconstructed nto three matrces u, s, and v usng SVD method. Later, the s matrc wll be embedded wth the watermark. Wth these methods, watermarked audo produce SNR>20dB and BER 0,1170 n average. Keywords: audo, watermarkng, SWT, DCT, SVD, QIM. 1. Introducton Now nformaton could be easly obtaned and uploaded n the nternet. As nternet s a complex lnks of computers around the world, securty and survellance of all ts content s very complcated. Many rresponsble people explot ths weakness to share data that s not thers to gan proft. Pracy s usually done to multmeda fles (audo, mage, and vdeo) and causes loss to the orgnal owner of that fle. That s why watermarkng methods s researched. Watermarkng s a technque n sgnal processng where a host data s embedded wth an nformaton or logo that works as an dentty of the data owner to protect the partcular data from pracy [2]. There are some qualtes whch a watermarked audo should have [1, 2]: 1) Inaudble. Watermark should not affect audo qualty wth sgnal-to-nose rato (SNR) value not more than 20 db; 2) Robust. Informaton that s embedded should last aganst attacks and manpulaton that s done by prates; 3) Secure. Watermark data that s embedded should not able to be extracted except by the maker. Transformaton doman has performance beneft compared to watermarks n tme doman because transform doman could better explot human audtory system. In ths paper three methods wll combned, Sngular Wavelet Transform (SWT) [3], Dscrete Cosne Transform (DCT) [4, 5], and Sngular Value Decomposton (SVD) to produce watermarked audo that s naudble, and robust aganst attacks lke nose addng, flterng, MP3 compresson, tme and frequency modfcatons. Many researchers develop hybrd algorthm to seek better performance. Chen and Zhu proposed a scheme that combnes DWT and DCT [6]. Two characterstcs of the transform were combned; DWT mult resoluton and DCT energy compresson. The embeddng s done through zerowatermarkng technque whch keeps the watermark n a secret key and not the sgnal. Another research of the same DWT-DCT hybrd algorthm also proposed to prevent damage from synchronzaton attack by nsertng the watermark nto hgh power low frequency component wth adaptve quantzaton [7]. Ths research combnes the three methods because of ths followng reasons; SWT s used to splt data nto two types hgh and low. The SWT process s used to protect the watermark from flterng attack. DCT s used to convert data nto frequency doman and also compress the data meanng that t could reduce the BER value produced. Then the obtaned DCT coeffcents wll be structured nto a symmetrcal matrx to be processed usng SVD to produce U, S, and V matrces whch also wll reduce the BER value produced. The S matrx then wll be used for embeddng usng QIM method. Optmzaton wth genetc algorthm s used n ths method snce each processng technque requre parameter nputs such as length of frame, threshold, number of bts used, and etc. genetc algorthm works to do combnaton tral of those parameters to see whch combnaton produce the best result.

2 The paper s structured as follows. The frst secton conssts of ntroducton; n ths secton the general explanaton of the paper s presented. Secton two wll tell you about the theoretcal bases of the proposed watermarkng scheme. The thrd secton wll talk about desgn and ts step by step processes. The fourth secton wll talk about result and analyss whle the last secton concludes the paper. 2. Theoretcal Bases 2.1. Statonary Wavelet Transform Wavelets Transformaton s used to decompose data nto two parts; hgh frequency data and low frequency data. The number of decomposton n ths process s usually decded by usage and the length of orgnal data. The data that s created from ths method s called SWT coeffcent. Orgnal fle could be reconstructed from those coeffcent trough nverse SWT process. SWT s a smplfcaton of DWT n whch there s no down samplng process n SWT. SWT sgnal s formed through a calculaton by passng through x sgnal nto several flters. The calculaton of approxmaton coeffcent ) and detal coeffcent could be calculated wth equaton 1 and 2 [9]: 1 x k c k = ( f ( x), ) (1) 1 / x k d k = ( f ( x), (2) 1 2 /2 2 Matchng wth the transformaton formula, the nverse process of approxmaton coeffcent of SWT can be calculated through equaton 3 [9]: 1 x k c k ( ), ( ) 1 = f x = h l c ( + 2)/2 ( + 2) (3) 1k + 2 l 2 2 l= Whle the detal coeffcent of SWT can be obtaned by equaton 4: d = h() l h (4) + 1 k 1k + 2 l = Sgnal s also decomposed smultaneously by usng hgh pass flter h. Result from both fltratons wll produce detal coeffcent (sgnal from hgh pass flter) and approxmaton coeffcent (sgnal from low pass flter). SWT could be appled n a nose removal applcaton n a sgnal, pattern scanner, bran mage classfcaton, and bran dsease detecton Dscrete Cosne Transform As n other transformatons, Dscrete Cosne Transform (DCT) ams to de-correlate audo data. Every transform coeffcent could be encoded ndependently wthout losng compresson effcency. DCT transform that wll be used n ths ournal s one dmensonal DCT defned as equaton 5 [7]. n 1 (2n 1)( k 1) y( k) = w( k) x( n)cos (5) n= 0 2N Where y(k) s the one dmensonal DCT coeffcent, x(n) s the orgnal fle audo, N s the length of fle audo and k equals to 0,1,2,,N-1. Matchng wth ts transformaton equaton, the nverse equaton (IDCT) shown on 6: n 1 (2n 1)( k 1) x( n) = w( k) y( k)cos n= 0 2N (6) Where n = 0,1,2,,N-1. In both formulas, w (k) s defned as equaton 7:, k = 0, (7) As such, the frst transform coeffcent s the sample sequence mean known as DC coeffcent. DCT normally used n sgnal or mage processng, especally lossy compresson because DCT has a strong energy compresson characterstc Sngular Value Decomposton SVD s a factorzaton process of a real or complex matrx. Is a generalzaton of Egen decomposton of asymmetrcal matrces (dfferent length and wdth). Say A s a matrx wth n x n sze. Through SVD process ths matrx can be decomposed followng equaton 8 [14]: n t A UDV SU V = 0 = = (8) Where U & V matrces are an n x n matrx whch s orthogonal and matrx s the dagonal elements of D wth postve real numbers. The nonzero value of the matrx s called sngular values of matrx A. Equaton 2.8 s devsed from the fact that A T A s symmetrcal. Thus ts egenvector form orthonormal bass. Take x egenvector and λ as ts egenvalue. Then take σ = and r = Ax Those varables the followng matrces could be bult; dagonal matrx S, wth σ as ts dagonal value, U matrx wth r as ts columns, and V matrx wth x as ts columns. If we multply U wth S, thus equaton 2.9 wll be created: σ r = σ = Ax = Ax (9) Then f we multply US wth V T would mean multplyng Ax wth rows of x. Then matrx consstng of Ax x T wll be created. Due to symmetrcal characterstc of the matrx x and x they wll form an orthonormal bass. Meanng that egenvector that s dfferent wll be orthogonal to each other. Then x x T = 0 f ( ) and x x T = 1 on

3 the same condton. That s why x x T wll form dentty matrx, because dagonal multplcaton wll produce 1 ( = ) whle on other poston zero wll be emulated ( ) whch means USV T = AI = A Quantzaton Index Modulaton QIM s a watermark embeddng method by usng two or more quantzer, where each quantzer has ts own ndex [6]. In desgnng, functon range should be desgned so each functon reach not overlaps each other. Ths s done so n extracton process, m value could be defned unquely. To reach the desred result, functon range should be dscontnued followng the quantzer characterstc. 10 are the formula for QIM Embeddng [9]: { Ak, fw= 1,arg mn F (0) Ak Bk, fw= 0,arg mn F (0) Bk (10) Wth F (o) = watermarked audo, F(o) = orgnal audo, w = watermark bt and & defned as follows: = (2k + 0.5) Δ and k = ±0, ±1, ±2, = (2k - 0.5) Δ and k = ±0, ±1, ±2, Whle the extracton formula s stated n 12 [9]: F(0) V ( k) = mod( cel 2) (12) Where s an estmate of nformaton n the receved sgnals Genetc Algorthm (11) Genetc algorthm s adapted from evoluton mechansm and natural genetcs [10] and works to fnd the best, most optmum parameter. Parameter fndng mechansm s done usng selecton, crossover and populaton mutaton process so a soluton called as chromosome could be produced. Chromosome buldng components s called as gen, where gen could be n a form of character, symbol, bnary or numerc number followng the problem wshed to be solved. Those chromosomes wll be contnually produces wth dfferent genes to produce the best output; ths evoluton s done contnuously n a generaton. Chromosomes wth a good success rate wll be further evolved n the next generaton. Chromosome success rate s measure by ftness functon (FF) parameter, so chromosome wth hgh FF value wll be chosen n the next generaton [11]. In a generaton, chromosomes produced by dong crossover between chromosomes. The number of chromosomes undergong crossover depends on the crossover probablty nputted. Besdes that, chromosomes could also be produced through mutaton or the alteraton of one or more gen value randomly (not through crossover). Mutaton probablty that s nputted decdes how many mutatons wll happen. In the end chromosomes wth convergent value wll produce the best soluton towards the problem [11]. 3. Desgn In ths paper there wll be three desgns of audo watermarkng algorthm usng text shaped mage watermark data. Those algorthms wll have the same transformaton methods whch are SWT-DCT- SVD but wll have a dfferent framng process. The desgns, whch wll be called A, B, and C desgn defned as follows: Desgn A: outsde SWT and DCT + SVD + QIM Desgn B: outsde SWT nsde DCT + SVD + QIM Desgn C: nsde SWT dan DCT + SVD + QIM Where outsde means that framng wll be done after SWT/DCT process. Whle nsde means that framng s done before SWT/DCT process. Part 3.1 wll explan general embeddng and extractng process of the desgned system regardless of when the framng process s done Embeddng Process Embeddng process s a process of nsertng watermark nto audo host. Inserton done through some steps shown n fgure 1. Step 1: Before embeddng, watermark mage s frst converted nto W vector n a sze of m x n. Then orgnal audo fle s sampled by samples per second samplng rate. Then sampled data s framed nto many. The sum of all frames equal all audo sgnals sampled wth equaton 13: A = A ; 1 < < N (13) Step 2: Do SWT transformaton on every A. Ths operaton wll create sub-bands wth same sze: Ds and A (s n Ds represent the number of sub-bands created). Where Ds represent detal sub-bands and A represent approxmaton sub-bands (see fgure 2). Step 3: Then transform those sub-bands usng DCT to produce Dx and Ax, whch are DCT transformed Ds and A. Those sub-bands wll be constructed nto a DC matrx wth s x (L/2) as ts sze, where L s the length of every frame and s s the number of sub-bands created by SWT. If the number of sub-bands created amounted to four, the DC matrx wll look lke fgure 3. Step 4: Then, decompose DC usng SVD operaton (see equaton 3.2). Ths operaton wll create three orthonormal matrces u, s, and v whch are factorzatons of DC matrx. DC = U x S x V T (14) Where s s dagonal 4 x 4 matrx wth non-zero sngular value n ts dagonal. Those values wll be used for embeddng. Step 5: Then S matrx wll be embedded usng QIM by modulatng ts value wth m ndex. The value of m could ranges from 1-10 depends on the system nput.

4 3.2. Extracton Process Extracton process s a process of retrevng watermark data from watermarked audo. Extracton process s the nverse of embeddng process and could be seen n fgure 4: Step 1: Take watermarked audo and sample t wth samplng rate of samples per second to avod alasng. Then frame sampled data nto many, n whch the sum of all framed data equals to all audo sgnal. Step 2: Perform SWT on every frame and produce approxmaton and detal coeffcents. Take the approxmaton coeffcent and process t wth DCT to produce wavelets. Step 3: The created wavelets wll be n a form of sub bands n regards to SWT output. Make a matrx out of those sub bands, a smlar one wth embeddng process. Then factorze the created matrx usng SVD to produce U, S, and V matrx. Step 4: Take the S matrx and extract t usng QIM extracton equaton and the watermark data wll be produced Genetc Algorthm Process In ths step, watermarkng parameters wll be optmzed to produce optmum output. Generally optmzng process wth genetc algorthm could be seen n fgure 5: Step 1: Intalze the parameter of the algorthm. Defne the number of generatons, ndvduals, mutaton and also crossover probablty that s desred. Step 2: Intalze the parameter to be optmzed; n ths paper those parameters are level decomposton of SWT, QIM quantzaton number, sze of frame n framng process, threshold of audo power that s allowed, and audo quantzaton bt. Step 3: After ntalzatons start the algorthm. The algorthm then wll start producng chromosomes (solutons) wth the parameter to be optmzed as ts genes. The calculaton of ftness functon wll be done through embeddng, attackng and extractng processes. As explaned on theoretcal bases, chromosomes wth good ftness functon wll be selected nto the next generaton and be used to produce new chromosomes trough crossover and mutaton processes. Step 4: When the generaton desred or ftness functon has reached the value of 1, the algorthm wll stop producng the chromosomes and the optmzed parameters wll be produced. 4. Result and Analyss 4.1. Early System Test and Analyss In early system test, tral by changng parameter combnaton wll be conducted. Ths experment wll Fgure 1. Embeddng Process Fgure 2. Coeffcents product of SWT Transform Fgure 3. Matrx DC Fgure 4. Extracton Dagram Fgure 5. Genetcs Algorthm Optmzng Process Dagram [9] conclude an analyss of how N (decomposton level), Nframe (frame number), Nbt (number of QIM bt), thr (threshold), and bt (quantzaton depth) affect the watermarked audo. Experments are held by changng the least sgnfcant parameters towards more sgnfcant parameters. Table 1 shows the ntal parameters Table 2 shows the result of early parameter test: Table 2. shows that desgn A produce the worst result compared to the other desgn. Whle B & C shows a good result wth BER = 0.

5 Table 1. Intal Parameter of Each Desgn Parameter Desgn A Desgn B Desgn C N Nframe Nbt Thr Typew Bt Table 2. Early Parameter Trals and Error Test Result Desgn N Nframe A B C Table 3. Desgn B robustness aganst attack Attack LPF (fc 3 khz, 6 khz, 9 khz) BPF (fc 100, 50, 10 6khz) Nose Addng (5db, 10db, 20db) Resamplng (11khz, 16khz, 22khz) Tme Scale Modfcaton (0.97s, 0.99s, 1.01s) Ptch Shftng (96s, 99s) MP3 Compresson (32, 64, 128, 256 kbps) Voce 0, Next B & C parameters wll be tested wth attacks. Shown n table 3 and 4 are the results of attacks aganst BER. From attack test result t could be concluded that desgn B and C s strong aganst LPF, BPF, nose addng and resamplng attacks shown from BER = 0 value that s constant even after attack but both desgn s weak aganst tme and frequency modfcaton and compresson attack Fnal System Test and Analyss nbt Thr Bt BER 10 0,1 16 0, , , Mean BER Value Desgn B Instrume class Rock Jazz nt c 0, 0, 0, 0, 0.41, 0.50, 0.38, 0.29, 0.19, , 0.29, 0.28, , 0.51, 0.54, 0.46, Early system test has shown desgns performance before and after attack. In fnal system test and analyss, all desgn wll be. optmzed wth genetc algorthm to fnd the best parameters combnaton After the best combnaton s obtaned the desgn wll be tested and be compared wth ts early test result. In ths test parameters that wll be optmzed are N, Table 4. Desgn C robustness aganst attack Attack LPF (fc 3 khz, 6 khz, 9 khz) BPF (fc 100, 50, 10 6khz) Nose Addng (5db, 10db, 20db) Resamplng (11khz, 16khz, 22khz) Tme Scale Modfcaton (0.97s, 0.99s, 1.01s) Ptch Shftng (96s, 99s) MP3 Compresson (32, 64, 128, 256 kbps) Ftness Functon -0,22516 Voce Mean BER Value Desgn C nstrume nt rock Jazz classc 0.51, 0.01, 0.40, 0.41, 0.46, , 0.41, 0.46, Table 5. Optmzed Parameters N 0.53, 0.52, 0.02, Nframe , 0.38, 0.38, 0.52, 0.39, 0.46, 0.13, 0.34, 0.50, 0.07, Nbt Thr bt 7 0,5 32 Nframe, nbt, thr, and bt. Genetc algorthm wll be used to search for the optmum parameter by usng these terms: Number of generaton = 300 Number of ndvdual = 20 Crossover probablty = 0.8 Mutaton probablty = 0.5 Audo = rock.wav Optmzed Attack = ptch shftng & MP3 compresson Rock audo and ptch shftng attack were chosen because both produce the worst BER. MP3 compresson was chosen because t s the most common attack used n current status quo. Optmzaton shows that desgn C (mean BER after attack = 0,1170) has better robustness compared to desgn B (mean BER after attack = 0,1553). Table 5 shows the obtaned optmzed parameters. Where the most robust audo s nstrumental.wav wth mean BER value = Fgure 6. Shows hgh resstance aganst attacks. Though the audo stll weak aganst ptch shftng and MP3 compresson. Table 6 shows the result of attack aganst optmzed nstrumental wav:

6 Table 6. Optmzed Parameter Aganst Attack Attack Parameter Or. BER (Des. B) nstrumental.wav Or. BER (Des. C) pt.ber (Des. C Extracted Image Ptch Fc 3 khz ,3900 LPF Fc 6 khz kbps ,3900 Fc 9 khz kbps ,3800 MP3 Fc khz kbps ,4100 BPF Fc Hz kbps Fc Hz Mean BER Value Db Nose 10 db db Hz Fgure 6. Ftness Functon to number of generaton graphc on Desgn C ptch shftng optmzaton Resample Hz Hz Both B and C desgns wth orgnal parameters are weak aganst tme scale modfcaton, ptch shftng and MP3 compresson (column 3 and 4, row 15 22). Through optmzaton we can see that the audo become more robust aganst tme scale modfcaton and ptch shftng to a certan level. Meanng that optmzaton create better robustness. TSM Tempo 0.97 s ,0100 Tempo 0.99 s Tempo 1.01 s 5. Concluson From the test that has been conducted t could be concluded that out of three desgned system, B desgn has the best performance and could also be concluded that framng that s done after DCT transformaton runs watermarkng result. Shown from A desgn that dd not produce any BER = 0, and framng that s done before both transformaton produce output that s slghtly worse compared to framng that s done after SWT. Watermarkng

7 scheme wth three transformatons of SWT-DCT- SVD could produce watermarked audo wth mean output value of BER = 01170, ODG 0,15 and SNR 30 db. Lastly, optmzng lowers BER value on ptch shftng, tme scale modfcaton and MP3 compresson and produce audo qualty that are better compared to before optmzng. References [1] R. F. Olanrewau and O. Khalfa, Dgtal Audo Watermarkng; Technques and Applcatons, Internatonal Conference on Computer and Communcaton Engneerng (ICCCE), 3-5. July, pp. 3 5, [2] B. Laurence, T. ahmed H., H. Khaled N. Dgtal Watermarks for Audo Sgnals, IEEE Int. Conference on Multmeda Computng and Systems, June 17-23, Hroshma, Japan, 1996, [3] Al-Ha. Al, T. Chrstna, and M. Ahmad, Hybrd DWT-DWT Audo Watermarkng, Ffth Internatonal Conference on Dgtal Informaton Management (ICDIM), 5-8 July, [4] Khayam, Syed Al, The Dscrete Cosne Transform (DCT): Theory and Applcaton, ECE : Informaton Theory and Codng, Department of Electrcal &Computer Engneerng, Mchgan State Unversty Semnar, March 10 th, [5] R. M. Zhao, H. Lan, H. W. Pang, B.N. Hu, A Watermarkng Algorthm by Modfyng AC Coeffcent n DCT Doman, Internatonal Symposum on Informaton Scence and Engneerng, pp IEEE, [6] N. Chen and J. Zhu, A Robust Zero- Watermarkng Algorthm for Audo, EURASIP Journal on Advances n Sgnal Processng, pp. 1-7, do: /2008/453580, Artcle ID , [7] X. Wang and H. Zhao, A Novel Synchronzaton Invarant Audo Watermarkng Scheme Based on DWT and DCT, IEEE Trans. Sgnal Processng, vol. 54, no. 12, pp , [8] S. Zhong and S. O. Oyad, Crack detecton n smply supported beams wthout baselne modal parameters by statonary wavelet transform, Mech. Syst. Sgnal Process., vol. 21, pp , [9] B. Chen and G. W. Wornell, Quantzaton ndex modulaton: A class of provably good methods for dgtal watermarkng and nformaton embeddng, IEEE Trans. Inf. Theory, vol. 47, no. 4, pp , [10] Shekh, R. H. M. M. Raghuwansh, and A. N. Jaswal, Genetc Algorthm Based Clusterng: A Survey, 2008 Frst Int. Conf. Emerg. Trends Eng. Technol., vol. 2, no. 6, pp , 2008 [11] Suyanto, Algortma Genetka Dalam Matlab, 1st ed. Yogyakarta: And Offset, [12] M. Sadeghzadeh and M. Taherbaghal, A New Method for Watermarkng usng Genetc Algorthms, Internatonal Conference on Machne Learnng, Electrcal and Mechancal Engneerng (ICMLEME 2014), January 8-9, pp. 1 8, [13] Y. Ln and W. Abdulla, Obectve qualty measures for perceptual evaluaton n dgtal audo watermarkng, Sgnal Process. IET, vol. 5, no. January, pp , [14] B. Boudraa and M. Hems, Dgtal Watermarkng n audo for copyrght protecton, Internatonal Conference on Advanced Computer Scence and Informaton System (ICACSIS), October [15] K.K Kothamasu, Seghal V.K, An Audo Watermarkng Algorthm Usng Encrypton, Transformatons and Quantzaton Internatonal Journal of Advances n Engneerng Scence and Technology, ISSN : /IJAEST/V2N1: [16] Ambka D., Radha V., Speech Watermarkng Usng Dscrete Wavelet Transform, Dscrete Cosne Transform And Sngular Value Decomposton Internatonal Journal of Computer Scence & Engneerng Technology (IJCSET), ISSN : , Vol. 5 No. 11 Nov 2014.