Research Article High Capacity Reversible Watermarking for Audio by Histogram Shifting and Predicted Error Expansion

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1 e Scentfc World Journal, Artcle ID , 7 pages Research Artcle Hgh Capacty Reversble Watermarkng for Audo by Hstogram Shftng and Predcted Error Expanson Fe Wang, 1 Zhaoxn Xe, 2,3 and Zuo Chen 1 1 School of Informaton Scence and Engneerng, Hunan Unversty, Lushan South Rood, Changsha 4182, Chna 2 College of Computer Scence and Technology, Zhejang Unversty, Hangzhou 3127, Chna 3 Chu Kochen Honors College, Zhejang Unversty, Hangzhou 3158, Chna Correspondence should be addressed to Zhaoxn Xe; xzxverson@126.com Receved 6 March 214; Accepted 22 Aprl 214; Publshed 14 May 214 Academc Edtor: Fe Yu Copyrght 214 Fe Wang et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Beng reversble, the watermarkng nformaton embedded n audo sgnals can be extracted whle the orgnal audo data can acheve lossless recovery. Currently, the few reversble audo watermarkng algorthms are confronted wth followng problems: relatvely low SNR (sgnal-to-nose) of embedded audo; a large amount of auxlary embedded locaton nformaton; and the absence of accurate capacty control capablty. In ths paper, we present a novel reversble audo watermarkng scheme based on mproved predcton error expanson and hstogram shftng. Frst, we use dfferental evoluton algorthm to optmze predcton coeffcents and then apply predcton error expanson to output stego data. Second, n order to reduce locaton map bts length, we ntroduced hstogram shftng scheme. Meanwhle, the predcton error modfcaton threshold accordng to a gven embeddng capacty can be computed by our proposed scheme. Experments show that ths algorthm mproves the SNR of embedded audo sgnals and embeddng capacty, drastcally reduces locaton map bts length, and enhances capacty control capablty. 1. Introducton Reversble watermarkng s a technque that means after watermark nformaton was embedded nto the host data, orgnal data can be restored whle watermark nformaton s extracted through a seres of processes. Because the reversble watermarkng schemes can acheve lossless recovery, they can be wdely used n mltary communcaton, medcal mage, and dgtal forenscs felds [1 3]. At present, the reversble watermarkng technology research manly concentrates on mage [4 6]. Tan [7] proposed an mage reversble watermarkng algorthm usng dfference expanson technque. Ths algorthm frst appled an nteger wavelet transform on two adjacent pxels n the mage and then embedded the watermark data n host pxel by expandng the dfference of adjacent pxel. Fnally, the algorthm restored pxel data through the nverse nteger transform. In [8], a reversble watermarkng algorthm was presented usng predcton error expanson. Frst, the algorthm predcted a pxel value accordng to three pxels adjacent to the current pxel and then extended the dfference between predcton and current value to acheve watermark nformaton embeddng. The algorthm has hgher embeddng capacty capablty and better qualty of embedded mage than Tan s scheme. Dgtal speech s one of the mportant forms of dgtal meda, but reversble watermarkng algorthm for dgtal audo s relatvely less. Reversble audo watermarkng schemes manly face the followng problems: (1) audo n WAV format s a floatng number ranged from 1 to 1. In order to transform these data, we must map the data to nteger space; (2) human audo system s more senstve than the human vsual system,soevenslghtalteratonofaudosgnalcaneaslybe perceved; (3) the process of audo sgnal s much trcker than dgtal mage [9]. In [1], Yan and Wang proposed a reversble audo watermarkng scheme based on predcton error expanson method. Frst, three prevous samples of the audo sample were multpled by nteger coeffcents ndvdually and then the sum of three results was used to

2 2 The Scentfc World Journal predct the current sample. Second, watermarkng nformaton was embedded by expandng the dfference between the predcton and current ampltude. Ths algorthm mapped the audo data to nteger nterval and acheved recovery of orgnal audo sgnals. In the algorthm, a locaton map was used to mark the samples wthout watermark bts. However, the method suffers from lack of embeddng capacty due to large auxlary embedded locaton nformaton. In [11], a reversble audo data hdng algorthm based on lnear predcton and error expanson was present by A. Nshmura. Relatve to [1], ths paper mproved the predcton coeffcents usng Burg method and obtaned a hgher SNR and embeddng capacty. However, ts capacty has the same drawback wth [1] and capacty control capablty was not consdered. Dfferental evoluton (DE) algorthm s an optmzaton algorthm based on swarm ntellgence. It can smulate the bologcal natural selecton and evoluton. Ths algorthm was frst present by Stron and Prce n 1995 [12]. DE algorthm can solve multparameters optmzaton wthout dependng on the contnuty of objectve functon and the conductvty of the constrants. Meanwhle, ths algorthm has good robustness and unversalty. It s wdely used n many felds [13 16]. The applcaton of hstogram to reversble watermarkng technquefrstappearednmagefeld.netal.[17]proposed a reversble watermarkng algorthm based on hstogram shftng, where watermark s embedded nto the hstogram bns. A novel watermarkng technque has been presented by Thod and Rodrígez n [18], where locaton nformaton s dramatcally compressed by usng hstogram shftng technque. To our best knowledge, however, reversble audo watermarkng based on hstogram technque has not been reported yet. So, we cannot apply the hstogram technque of magefeldtoaudodatadrectly. In ths paper, we proposed a novel reversble audo watermarkng scheme based on mproved predcton error expanson and hstogram shftng whch combned wth the characterstcs of dgtal audo data. Dfferent from usng the fxed set of lnear predcton coeffcents, we propose the optmzaton model of predcton coeffcents and then use the dfferental optmzaton algorthm to determne the optmal lnear predcton coeffcents, so that the overall predcton errors are as small as possble. In addton, accordng to the characterstcs of audo data, we ntroduced hstogram shftng technque whch can dramatcally reduce the embeddng locaton nformaton. Meanwhle, we present the method whch can compute the predcton error modfcaton threshold accordng to a gven embeddng capacty. The experments show that ths algorthm can enhance capacty control capablty. The organzaton of ths paper s as follows. In Secton 2, we propose predcton error expanson based on dfferental evoluton. Secton 3 descrbes how nformaton embeddng and extracton are done by usng hstogram shftng and predcton error expanson. The experment results and analyss are presented n Sectons 4 and 5, respectvely.the conclusons are n Secton Predcton Error Expanson Based on Dfferental Evoluton 2.1. Dfferental Evoluton Algorthm. Dfferental evoluton algorthm has the advantages of hgh effcency, good convergence, and robustness. It can perform well when deal wth multparameter optmzaton problem, especally when the soluton space s stochastc. Dfferental evoluton algorthm can be dvded nto the followng operatons. (1) The mutaton: the mutaton of DE s drven by the dfference between the parent ndvduals; the mutaton of vector u g s as follows: V g+1 =u g r1 +F(ug r2 ug r3 ), r 1 =r 2 =r 3 =, (1) where r 1, r 2, r 3 are unform random ntegers [1, N p ], F [,2]represents the control parameters, and g means the ndex of generaton. (2) Crossover: n the DE algorthm, the crossover operaton guarantees the dversty of populaton. When applyng crossover on mutant vector V g+1 and current ndvdual u g, we can get new vector wg+1,j. The formula of crossover operaton s as follows: w g+1,j ={ Vg+1,j, (rand () C R or j=l ) u g,j, otherwse, (2) where j (1,2,...,D) and D s the dmenson of the problem, rand () means the random values [, 1], andc R represents the probablty of crossover operaton. l [1,2,...D]s a randomly chosen value. (3) Selecton: the selecton operaton of DE algorthm s basedonagreedyselectonscheme.wechoosethe ndvdual whch has better ftness between the vector w g+1 and the ndvdual of orgnal populaton u g.the formula s descrbed as follows: u g+1 ={ wg+1, f(w g+1 ) f(u g ) u g, otherwse, where f( ) represents ftness functon The Optmzaton of Lnear Predcton Coeffcents Based on DE Algorthm. Fgure 1 presents a typcal hstogram of predcton errors for classc musc. The predcton errors hstogram for most natural audo would be smlar to ths. Consder a process of expanson embeddng. The smaller the magntude of the predcton errors, the smaller the dstorton oftheaudo.inthesense,thepredctonerrorsoccurmore frequently n the central regon. Snce embeddng capacty and dstorton performance are most mportant ndcators for reversble audo watermarkng scheme, the optmzaton am s to maxmze SNR of embedded audo and capacty. The formula s presented as follows: (3) L =1 F=1log 1 ( S2 () L =1 [S2 () S 2 ()] ) l L, (4)

3 The Scentfc World Journal Classc Inner regon Outer regon Outer regon 2 Occurrence 15 1 Occurrence Dfference Fgure 1: Hstogram of predcton errors for classc musc. where S 2 () and S 2 () mean orgnal and embedded audo, respectvely, L s the length of the audo data, and l means the number of embedded bts. 3. Predcton Error Expanson Based on Hstogram Shftng 3.1. Hstogram Shftng. In [18], hstogram of predcton error for a host data s dvded nto two nonoverlappng regons, the outer and the nner regons, whch are shown n Fgure 1. Expanson of the predcton errors expands the hstogram of the nner regon. As s shown n Fgure 2, afteraprocessof expanson, the peak of the hstogram has fallen by almost 5%. Because the hstogram of predcton errors for audo does not strctly satsfy the contnuty, the algorthm n [18], n whch the determnaton of embedded locaton was based on symmetrcal hstogram, cannot be appled to audo reversble watermarkng algorthm. In ths paper, we assume two thresholds T l and T r,whchcanbeusedtocontrolthe boundares of the nner regon and outer regon. We frst set T l =, T r = 1. Gven a capacty, we can determne the boundares of nner regon and outer regon accordng to the Algorthm 1. Where D means all the predcton errors, functon num ( ) returns the ndex of the gven value. In the process of determnng the thresholds, we consdered the cumulatve mpact by all expansve bns rather than only the current expansve bn Embeddng and Extracton. All steps of the watermark embeddng procedure based on mproved predcton error expanson and hstogram shftng are shown n Fgure 3.The detals are descrbed as follows. Step 1. Assume a host audo data A = {a 1,a 2,a 3,...,a N a [ 1,1]}.Foraudosamplea, we defne a 3,a 2,a 1 as Dfference DE DE-embed Fgure 2: Hstogram after expanson of nner regon and orgnal predcton errors. sort(d) set T l =,T r =1 set =num(t l ), j = num(t r ) whle(p < P set ) f (num l + num())/cnt l < (num r + num(j))/cnt r P=P+num(j + 1) num r=num r+num(j + 1) j=j+1 cnt r=cntr+1 else P=P+num( 1) num l=num l+num( 1) = 1 cnt l=cntl+1 end T l =D() T r =D(j) Algorthm 1 ts neghborhood samples and a as ts predcted value. As n formula (5), F( ) represents predctor: a =F(a 3,a 2,a 1 ). (5) Step 2. Defne p as predcton error p = a a ;thenwe embedded watermarkng bt b by expanson of predcton error p. The formula s as follows: p =2 p +b. (6) Step 3. Determne the lnear predcton coeffcents based on DE algorthm. We can defne F( ) = round(c 1 a 3 +c 2 a 2 +c 3 a 1 )(>3). Accordng to the optmzaton scheme

4 4 The Scentfc World Journal Orgnal audo Watermark nformaton Generate lnear predcton coeffcents usng DE algorthm Encrypton Determne threshold of modfcaton predcton errors Locaton map Lossless compresson Buld embedded bts Usng expanson of predcton errors and hstogram shftng to embed nformaton nto audo data Fgure 3: Dagram of watermark embeddng process. proposed n Secton 2, wecanobtanoptmalpredcton coeffcents C=[c 1best,c 2best,c 3best ]. Step 4. Gve capacty P.WecanobtanthresholdT l,t r based on Algorthm 1. Step 5. Embed watermarkng nformaton usng hstogram shftng and expanson of predcton errors. T eml and T emr are left and rght boundares of hstogram. We can obtan nner regon [T l,t r ] and outer regons [T eml,t l ) (T r,t emr ]. Fnally, embed the watermarkng nformaton usng expanson of predcton errors or acheve locaton markng usng hstogram shftng. The formula s descrbed as follows: p { abs (T l ) f p [T eml,t l ) p = 2 p { +b else f p [T eml,t emr ] { p + abs (T r )+1 otherwse. Step 6. Buldlocatonmap.Sncea [32767, 32768], n order to prevent overflow and underflow problems durng embeddng process, we assgn a value 1 n locaton map when the above stuatons occur. Otherwse we assgn a value. The locaton map should be lossless compressed. We use run-length codng and Huffman codng scheme. In the process of decodng, we can extract the watermark nformaton and restore host audo data. The detals are as follows. Step 1. The current audo sample s obtaned from (5)byusng restored host data a() ( > 3). If the value of locaton map s 1, keep the audo sample unchanged. For predcton errors (7) ranges n [2T l,2t r +1], we can extract the watermark bt and restore audo sample usng b=mod (p,2), p = p b, 2 a =a p. Step 2. For hstogram shftng, we can restore the audo data by the followng formula: p ={ p + abs (T l) f p <2 T l p abs (T r) 1 f p >2 T r Experment Set In ths secton, we choose fve common audo types to demonstrate the performance of the proposed algorthm. All audo fles are 16-bt mono n wave format and the samplng rates of them are 44.1 khz. The detaled descrpton s lsted n Table 1. For optmzaton of lnear predcton coeffcents basedondealgorthm,wesettheszeofntalpopulaton to 65. The teraton s set to 5. The probablty of crossover and mutaton are.9 and.9, respectvely Evaluaton Method. In order to evaluate the performance of the proposed algorthm, we choose two common ndcators SNR and capacty. SNR s a statstcal dfference metrc (8) (9)

5 The Scentfc World Journal 5 1 Orgnal audo sgnal 1 Watermarked host audo sgnal Ampltude.5.5 Ampltude Tme Tme 1 5 (a) (b) Fgure 4: Orgnal pop audo and the watermarked audo sgnal. Table 1: Test database 16 bts. Index Audo descrpton 1 Blues 2 Classc 3 Jazz 4 Pop 5 Country to measure the perceptual smlarty between orgnal audo andembeddedaudo.theformulasasfollows: SNR (S, S L =1 ) =1log 1 ( S2 () L =1 [S () S ()] 2 ), (1) where S(), S () are orgnal audo sample and embedded audo sample, respectvely. Capacty s used to measure how much nformaton can be embedded nto a host data. The formula s as follows: where N L means the requred number of audo sample when embeddng one bt watermark durng embeddng procedure p= 1 N L. (11) 5. The Expermental Results and Analyss Usng the ED algorthm settngs n Secton 4,theconvergence curves of object functon wth DE algorthm are shown n Fgure 5. The best ftness of an ndvdual can converge fast to a stable value n the twenty-ffth generaton. The comparson of SNR and capacty between embedded audo usng the optmal lnear predcton coeffcents and best lnear predcton coeffcents proposed n [1] wasshownn Table 2.We can see that the lnear predcton coeffcents generated by DE algorthm acheve better SNR and capacty than fxed coeffcents for all the types of test audos n the same condton. Fgure6 presents the performance of the 64 predcton coeffcent sets and optmal predcton coeffcent setforclasscaudo.itcanbeseenthatlnearpredcton coeffcents obtaned by DE have obvous advantage n SNR and capacty. The orgnal and watermarked audo sgnals n tme doman are shown n Fgure 4; the dfference between them s nvsble. The best ftness Generatons Fgure 5: Convergence curves of object functon wth DE algorthm. Table2:ThecomparsonofSNRandcapactybetweenembedded audo usng the optmal lnear predcton coeffcents and best lnear predcton coeffcents proposed n [1]. Set T = 5 Audo Ours [1] SNR Capacty SNR Capacty Blues Classc Jazz Pop Country Fgure 7 graphcally depcts the embeddng capacty versus the embedded audo qualty curve of fve types of audo. We can see that the SNR of embedded audo decreased wth the ncrease of embeddng capacty. Accordng to the gven embeddng capacty, we can convenently determne and modfy the threshold of predcton error. That enhances capacty control capablty. In order to evaluate the performanceofproposedalgorthmbasedonhstogramshftng, we use run-length codng and Huffman codng scheme to

6 6 The Scentfc World Journal Audo qualty SNR (db) Classc Set of parameters Alg. P1 Alg. DE (a) Payload sze (bpp) Set of parameters Alg. P1 Alg. DE (b) Fgure 6: The performance of the 64 predcton coeffcent sets and optmal predcton coeffcent set for classc audo. SNR (db) Locaton map length Embeddng rado (bts/sample) Country Jazz Pop Blues Classc Fgure 7: The embeddng capacty versus the embedded Embeddng rado (bts/sample) Country Jazz Pop Blues Classc Fgure 8: The embeddng capacty versus compressed locaton map length curve of fve types audo qualty curve of fve types audo. compress the locaton map.fgure 8 presents the embeddng capacty versus compressed locaton map length curve of fve types of audo. The locaton map s generated usng algorthm proposed n [11]. We can see that locaton map of algorthm compared has a large length and the compressblty of locaton map s low. The large auxlary locaton map wll take a lot of capacty. In ths paper, we adopt hstogram shftng to reduce the locaton map. As we know, we can map the audo data to nteger ranged from to As the range of audo data s very wde, the overflow phenomenon rarely occurs. Meanwhle, the locaton map of proposed algorthm only needs overflow locaton nformaton, so the length of locaton map can be reduced drastcally. The experments showed that the length of locaton map usng proposed algorthm s neglgble. 6. Concluson Ths paper proposed a novel reversble audo watermarkng algorthm based on mproved predcton error expanson and hstogram shftng. As the performance of reversble audo scheme usng predcton error expanson s affected by the predctor, we proposed the optmzaton of lnear predcton coeffcents usng dfferental evoluton algorthm. The experments have shown that the proposed scheme can acheve better embedded audo qualty and hgher capacty. In order to reduce the length of locaton map, we ntroduced hstogram shftng scheme. In addton, we proposed the scheme whch can be used to compute predcton error modfcaton threshold accordng to a gven embeddng capacty. The smulaton results verfy our scheme can drastcally

7 The Scentfc World Journal 7 reduce locaton map bts length and enhance capacty control capablty. Conflct of Interests The authors declare that there s no conflct of nterests regardng the publcaton of ths paper. Acknowledgments Ths work was sponsored by Natonal Nature Scence Foundaton of Chna ( ), Specalzed Research Fund for the Doctoral Program of Hgher Educaton, Chna ( ), and the Young Teacher s Growth Program of Hunan Unversty. References [1]J.B.Feng,I.C.Ln,C.S.Tsa,andY.P.Chu, Reversble watermarkng: current status and key ssues, Internatonal Network Securty,vol.2,no.3,pp ,26. [2] M.Vogt,B.Yang,andC.Busch, Reversblewatermarkngof 2D-vector data, n Proceedngs of the Multmeda and Securty Workshop, pp , ACM, September 24. [3] D. Coltuc, Low dstorton transform for reversble watermarkng, IEEE Transactons on Image Processng, vol. 21, no. 1, pp , 212. [4] S.Lee,C.D.Yoo,andT.Kalker, Reversblemagewatermarkng based on nteger-to-nteger wavelet transform, IEEE Transactons on Informaton Forenscs and Securty,vol.2,no.3,pp , 27. [5] L.Luo,Z.Chen,M.Chen,X.Zeng,andZ.Xong, Reversble mage watermarkng usng nterpolaton technque, IEEE Transactons on Informaton Forenscs and Securty, vol.5,no. 1,pp ,21. [6] D. Coltuc and J.-M. Chassery, Very fast watermarkng by reversble contrast mappng, IEEE Sgnal Processng Letters,vol. 14, no. 4, pp , 27. [7] J. Tan, Reversble data embeddng usng a dfference expanson, IEEE Transactons on Crcuts and Systems for Vdeo Technology,vol.13,no.8,pp ,23. [8] D. M. Thod and J. J. Rodríguez, Reversble watermarkng by predcton-error expanson, n Proceedngs of the 6th IEEE Southwest Symposum on Image Analyss and Interpretaton,pp , March 24. [9] H. Zhao, F. Wang, Z. Chen, and J. Lu, A robust audo watermarkng algorthm based on SVD-DWT, Electroncs & Electrcal Engneerng,vol.2,no.1,pp.75 8,214. [1] D. Yan and R. Wang, Reversble data hdng for audo based on predcton error expanson, n Proceedngs of the 4th Internatonal Conference on Intellgent Informaton Hdng and Multeda Sgnal Processng (IIHMSP 8), pp , August 28. [11] A. Nshmura, Reversble audo data hdng usng lnear predcton and error expanson, n Proceedngs of the 7th Internatonal Conference on Intellgent Informaton Hdng and Multmeda Sgnal Processng (IIHMSP 11),pp ,October 211. [12] R. Storn and K. Prce, Mnmzng the real functons of the ICEC 96 contest by dfferental evoluton, n Proceedngs of the IEEE Internatonal Conference on Evolutonary Computaton, pp ,1996. [13] J. Vesterstrøm and R. Thomsen, A comparatve study of dfferental evoluton, partcle swarm optmzaton, and evolutonary algorthms on numercal benchmark problems, n Proceedngs of the Congress on Evolutonary Computaton (CEC 4), vol.2, pp , June 24. [14] B. V. Babu and R. Angra, Modfed dfferental evoluton (MDE) for optmzaton of non-lnear chemcal processes, Computers and Chemcal Engneerng,vol.3,no.6-7,pp , 26. [15]B.Lu,J.Lu,Y.Wang,andY.Tang, Aneffectveparameter extracton method based on memetc dfferental evoluton algorthm, Mcroelectroncs Journal, vol.39,no.12,pp , 28. [16] W. Zhang, X. Wang, B. Lu, and T. Km, Secure encapsulaton and publcaton of bologcal servces n the cloud computng envronment, BoMed Research Internatonal,vol.213,Artcle ID 1758, 8 pages, 213. [17] Z. N, Y.-Q. Sh, N. Ansar, and W. Su, Reversble data hdng, IEEE Transactons on Crcuts and Systems for Vdeo Technology, vol.16,no.3,pp ,26. [18] D. M. Thod and J. J. Rodríguez, Expanson embeddng technques for reversble watermarkng, IEEE Transactons on Image Processng, vol. 16, no. 3, pp , 27.

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