Using Counter-propagation Neural Network for Digital Audio Watermarking

Transcription

1 Usng Counter-propagaton Neural Network for Dgtal Audo Watermarkng Chuan-Yu Chang and Wen-Chh Shen Graduate School of Computer Scence and Informaton Engneerng Natonal Yunln Unversty of Scence & Technology Doulou, Yunln, Tawan Abstract Recently, the watermarkng s an mportant technque to protect copyrght, whch allows authentc watermark to be hdden n multmeda such as dgtal mage, vdeo and audo. Watermarkng has been developed to protect dgtal meda llegal reproductons and modfcatons. Tradtonally, watermarkng requre complex procedures to embed and to extract watermark, such as randomzng the watermark, choose postons to embed and extract the watermark, embed the randomzed watermark nto the orgnal audo and extracted the watermark from the specfc postons. Therefore, n ths paper, we propose a scheme called Counter-propagaton Neural Network (CNN) for dgtal audo watermarkng. Dfferent from the tradtonal methods, the watermark s embedded n the synapses of CNN nstead of the orgnal audo sgnal. The expermental results show that the has capabltes of robustness, mperceptblty and authentcty. Keywords: Dgtal watermark, Counter-propagaton neural network, Audo nformaton hdng.. Introducton The rapd growng of computer network and multmeda technologes makes t easer to access dgtal meda. Therefore, t s urgent to protect copyrght pracy. To tackle the copyrght protects problem, many dgtal watermarkng schemes have been proposed[-4]. The watermarkng technque embeds owner s nformaton nto dgtal meda and provdes the correspondng authentcaton mechansm. Therefore, the dgtal watermarkng should has capabltes of robustness, mperceptblty and authentcty. Smlarly, the audo watermarkng should be naudble or statstcal nvsble to prevent unauthorzed detecton and removal. In general, the audo watermarkng technques can be classfed nto two groups, embeddng watermark n tme doman and embeddng watermark n frequency doman, dependng on the processng doman of the host audo n whch the watermark s embedded. In tme doman, the pre-maskng and post-maskng effect of tme doman s used to mplement audo watermarkng. Le et al. [8] proposed a method, whch based on the relatve energy relatons between adjacent sample sectons, to embed dgtal watermarks nto a host audo sgnal n the tme doman. However, ther method cannot resst four types of attacks, such as the AAC compresson, WMA compresson, Ampltude compresson and resamplng. In frequency doman, Jong et al. [6] proposed a watermark embeddng scheme accomplshes the perceptual transparency after watermark embeddng by explotng the maskng effect of the human audtory system (HAS). Lee et al. [7] proposed a dgtal audo watermarkng technque n the cepstrum doman. They nsert a dgtal watermark nto the cepstral components of the audo sgnal usng a technque analogous to spread spectrum communcatons, hdng a narrow hand sgnal n a wdeband channel. Recently, neural networks, wth ther features of fault tolerance and potental for adaptve tranng, have been proposed as alternatve approaches. Yang et al. [4] proposed an artfcal neural network-based scheme for watermarkng of audo sgnals. They use a mult-layer perceptron (MLP) to estmate the watermark scalng factor (WSF) ntellgently from the knowledge of host audo sgnal. Despte that, all the mentoned methods [4,6,7,8] were lack of robustness n audo watermarkng. In addton, the tradtonal methods need complex embeddng and correspondng extracton procedures. Therefore, n ths paper we proposed a specfc counter-propagaton neural network (CNN) for audo watermarkng. Dfferent from the tradtonal methods, the watermark s embedded n the synapses of CNN nstead of the orgnal audo sgnal. Therefore, the qualty of the watermarked audo s almost same as the orgnal audo sgnal. In addton, because of the watermark s stored n the synapses, most of the attacks are not degrade the qualty of the extracted watermark mage. The CNN substtutes the complex embeddng and correspondng extracton procedure. The expermental results show that the proposed CNN does not need complex embeddng and correspondng extracton procedure. Furthermore, the has capabltes of robustness, mperceptblty and authentcty. The remander of ths paper s organzed as follows. Secton shows the preprocessng of

2 orgnal audo sgnal. The archtecture of the CNN and the algorthm of embeddng, and extractng method are shown n Secton 3. Secton 4 summarzes the expermental results. Fnally, conclusons are gven n the Secton 5.. Preprocessng of orgnal audo sgnal In order to resst the malcously attacks, the orgnal audo sgnal s frstly segmented nto several non-overlappng frames wth frame sze of N. The synchronzaton code sequence s ntalzed the startng pont poston [8]. Ths assumpton wll be volated when an attacker tres to crop the sgnal or nsert redundancy n the front end. The audo energy s usng to evaluate the adequate frame to embed watermark, the energy of each frame s calculated as: n E ( k ) = n = s( ), k =,,..., N () where E(k) denotes the energy of frame k, and the s() represents the magntude of audo sgnal and n s the number of frame sze. The canddate frames set X (k) s obtaned by threshold of energy. The threshold s to search the poston of the non-quet sound segment, and sutable embeddng the watermark. The qualty of the extracted watermark s hghly dependng on the preprocessng procedure. Fgure shows the schematc dagram of the preprocessng. After the preprocessng, each canddate frame data X can be represented as a vector form: X = { x, x,..., x n} () where n s the length of canddate frames. Orgnal Audo Frame segmentaton Synchronzaton code E(n)>T Fgure.The schematc dagram of the preprocessng. False True Canddate Frame X(n) 3. Counter-propagaton neural network for audo watermarkng The tradtonal watermarkng methods requre complex embeddng and correspondng extracton procedures. In ths paper, a counter-propagaton neural network-based (CNN) audo watermarkng method s proposed. The proposed watermarkng method ntegrated the embeddng and extracton procedure nto a counter-propagaton neural network. Fgure shows the archtecture of the CNN. The CNN s conssts of n nput neurons to represent the length of canddate frame data, N hdden neurons to represent the total pxels of the watermark mage and m output neurons to represent the gray value of the watermark mage. In order to ensure that the proposed CNN has capablty of embeddng and extractng watermarks, the canddate frame data and the correspondng watermark are used to tran the CNN. After the network s evoluton, the watermark s embedded nto the synapses between the hdden layer and output layer. Audo Data X x x xn Input layer Fgure. The archtecture of the counter-propagaton neural network for watermarkng. There are two sets of weghts that are adjusted wth two dfferent learnng algorthms, the Konhones s self-organzng learnng and the Grossberg s supervsed learnng. In the frst stage, weghts W connectng the canddate frame data X and the hdden layer are traned usng Kohonen s self-organzng learnng rule, and acheve the goal to classfy tranng pattern to hdden layer. In the second stage, the weghts between the hdden layer and the output layer are traned usng Grossberg s learnng algorthm. The nput canddate frame vector X s fully connected to the neurons n hdden layer wth weghts W. W = w, w,..., } (3) where w j, w, w N,n Konhones s layer z z zn Hdden layer {,, w N, n denotes the weght between j-th neuron and nput x, =,,..., n. Accordngly, the total nput of the j-th neuron s defned as: / Extracted Watermark Y u, u m,n Grossberg s layer n Z j = ( x w j, ), j =,,..., N (4) = whch represents the total dstance between the nput X and the weghts of the j-th hdden neuron. After the dstances are computed, a wnner-takes-all mechansm s appled to the neurons n the hdden layer to fnd the wnner neuron unt to update weghts n the konhones s layer [5]. The wnner of the hdden neuron H * s defned as: H* = mn Z j, j =,,..., N (5) accordngly, the weghts between the nput layer and wnner neuron n hdden layer s update by: y y ym Output layer

3 ( x w ( )) wh *, ( k + ) = wh *, ( k) + η ( k) H*, k (6) where η(k) s learnng rate. In addton, the learnng rate η(k) s decreased gradually durng the tranng epoch k. The learnng functon η(k) can be specfed as follows: k η ( k ) = η ( 0 ) exp (7) k 0 where η (0) s ntal learnng rate, k 0 s a postve constant. The output of the CNN can be represented as vector form: Y = y, y,..., y } (8) { m where m s the bt length of watermark mage n bnary representaton. Each output neuron receves all the output of hdden neurons wth weghts U. U = u, u,..., } (9) where {,, u m, N u l, j denotes the weght between the j-th neuron n hdden layer and the l-th output neuron. Thus, the output of the -th neuron n the output layer s obtaned as y = N j= u Z, j j (0) Snce the WTA, only the wnng neuron H * n the hdden layer has output value one. Thus, the output of the -th output neuron can be smpled as: y = () * u,h Therefore, only the weghts connectng the wnnng neuron n the hdden layer wth the neurons n the output layer wll be updated. Accordngly, the weghts n the output layer of the CNN are updated as: u * ( k + ) = u * + η ( k) ( desre y ( )) () k l, H l, H l l where desre l s pxel value of the watermark mage. The learnng functon η(k) s calculated by Eq (7). In addton, the nstantaneous output error ξ q s defned as: m ξ = y desre (3) q l= l l Thus, the total error of the CNN s defned as N ξ = ξ (4) total q= q where N denotes the total pxels of the watermark mage. 3. Embeddng algorthm The watermark embeddng approach s summarzed as follows: Input: The canddate frame data X. Output: The watermark mage wll be embedded nto the synapse (W, U) of the converged CNN. Step.Arbtrarly assgns the ntal weghts to W and U. Step.Use Eq. (4) to calculate the output of each hdden neuron. Step 3.Use Eq. (5) to fnd the wnner neuron. Step 4.Update the weghts W accordng to Eq. (6). Step 5.Use Eq. () to update weght U. Step 6.Use Eq. (4) to calculate the output error of CNN. If ξ total s less than a predefned threshold value, stop tranng. Otherwse, go to Step. 3. Extractng algorthm To extract watermark from the traned CNN descrbed n Secton 3., the watermark extractng approach s summarzed as follows: Input: The canddate frame data X. Output: watermarked mage Y. Step.Use Eq. (4), Eq. (5) to calculate the output of the wnner neuron n hdden layer. Step.Use Eq.(8) and Eq.() to obtan the graylevel (n bnary) of the watermark mage Y. 4. Expermental results To show the proposed CNN has good capablty for audo watermarkng, four experments are presented to demonstrate robustness, mperceptblty and authentcty. In our experments, 6bts mono-track audo musc wth samplng rate 44. khz was used for smulaton. We selected three dfferent types of song from the database randomly. Fgures 3(a-c) show the orgnal audo wave of female snger, male snger and chorus, respectvely. Fgures 4 (a-b) show the 3 3 bnary rabbt mage and the 3 3 grayscale YUNTECH watermark mage, respectvely. In order to evaluate the robustness of the Le s method [8] and the proposed CNN method, two publc doman software: GoldWave and ImTOO are used to attack the watermarked audos. Eght types of attack are performed to attack the watermarked audos: Type : MP3 compresson, compress audo sgnal to 8k bt/s (wav->mp3->wav). Type : AAC compresson, compress audo sgnal to 8k bt/s (wav->aac->wav), MPEG-. Type 3: WMA compresson, compress audo sgnal to 8k bt/s (wav->wma->wav). Type 4: Ampltude compresson, multpler 6 db Type 5: Smoother flter. Type 6: Resamplng (44kHz->kHz ->44kHz). Type 7: Remove nose (clearly audble). Type 8: Slence reducton, reducton -48 db. The Normalzed Correlaton (NC) s used to evaluate the smlarty measurement of extracted

4 bnary watermark, whch can be represented as: P (, j ) P (, j ) (5) j NC = [ P (, j ) ] j where P (, j ) and P (, j ) represent the bt value of (,j)-th pxel of orgnal watermark and extracted watermark mage, respectvely. The Peak Sgnal to Nose Rato (PSNR) s used to evaluate the qualty of watermarked audo, whch can be represented as: PSNR ( db ) = 0 log 0 X peak (6) σ e where s defned as: σ e M ( X ( ) Z() M = σ e = ) (7) where M s the length of the host audo, X () s the magntude of host audo at tme. Smlarly, Z() denotes the magntude of watermarked audo at tme. X peak denotes the squared peak value of host audo. The hgher PSNR means that the watermarked audo s more smlar to the orgnal audo. frame N s 300. The ntal d of the Le s method [8] s set to The bnary rabbt watermark s embedded nto three orgnal audos. In order to evaluate the robustness of Le s method, the eght knds of attacks were appled to the watermarked audos. Fgures 5-7 show the extracted watermarks of the female snger, male snger, and chorus snger that suffered from eght dfferent type attacks. Obvously, Le s method cannot extract the complete watermark from varous attacks, such as AAC, WMA, Ampltude compresson, and resamplng result n messy pattern. In other words, the hded watermark has been destroyed by varous attacks. Fgure 5. The extracted watermarks of the watermarked audo by female snger (a) (b) Fgure 6. The extracted watermarks of the watermarked audo by male snger (c) Tme ( sec.) Fgure 3. Orgnal Audo Sgnals, (a) female snger, (b) male snger, (a) chorus snger. (a) (b) Fgure 4. Watermark mages, (a) bnary Rabbt mage (b) gray scale YUNTECH logo mage. 4. Experment : Robust testng for Le s method In ths experment, the watermark mage s a 3 3 bnary rabbt mage and the sze of audo Fgure 7. The extracted watermarks of watermarked audo chorus snger. Table. NC and bt error of extracted watermark from dfferent type of attack wth watermarked audo by Le s method NC of extracted bt error(3x3 bt) watermark female male chorus female male chorus Table shows the NC values and the bt error of Type

5 the extracted watermarks. From Table, the NC values are between 0.54 to The low NC values ndcate that the Le s method was unable to resst type (,3,4,6) attacks. 4. Experment : Robust testng for Fgure 8. The extracted watermarks of watermarked audo female snger. Fgure 9. The extracted watermarks of watermarked audo male snger. Fgure 0. The extracted watermarks of watermarked audo chorus snger. Table. NC and bt error of extracted watermark from dfferent type of attack wth watermarked audo by proposed CNN method NC of extracted bt error(3x3 bt) watermark Type female male chorus female male chorus In ths evaluaton, the ntal learnng rate η s set to.35. The number of neuron s 3 3 (sze of watermark mage). The threshold value of was establshed to termnate tranng. In order to evaluate the robustness of, eght knds of attacks are appled to degrade the watermarked audo. Fgures 8-0 show the extracted watermarks of watermarked audo that suffered from the same attacks as the prevous experment. Obvously, all the watermarks are extracted completely by usng CNN. Table shows the NC value and the bt error of extracted watermarks. It ndcates the proposed method s robust and beng able to resst eght attacks. 4.3 Experment 3: Imperceptblty for To show the mperceptblty of the proposed method, a bnary rabbt watermark and a gray scale Yuntech logo mage are embedded nto the three songs, respectvely. However, only a bnary rabbt watermark s embedded nto the three songs n Le s method. Table 3 shows the PSNR values of the watermarked audos by Le s and the proposed method. The average PSNR value of the Le s method s about 3dB. On the other hand, the PSNR values of the are all nfnte. The nfnte PSNR values llustrate that the watermarked audo sgnal s the same as the orgnal audos. Ths s because of the watermark s embedded n the synapses of CNN nstead of the orgnal audo sgnal; therefore, the qualty of the watermarked audo s almost same as the orgnal audo sgnal. It ndcates the s able to mperceptblty. Fgure (a-c) shows the extracted correspondng watermarks. These fgures show that the proposed method s capable of embeddng/extractng gray level watermark nto/from audo. Table 3. PSNR of watermarked audo by Le s method and the female male chorus Watermarked bnary mage by Le s method Watermarked bnary mage by Watermarked graylevel mage by 3.88dB 30.9dB 33.4dB (a) (b) (c) Fgure. The extracted watermarks from attack-free female, male and chorus snger, respectvely. 4.4 Experment 4: Authentcty testng In ths experment, we use raw audo, audo wthout embedded watermark, to test authentcty of the proposed CNN and Le s method. Fgure (a-c) and Fgure 3(a-c) show the extracted watermarks

6 from Le s and the, respectvely. Obvously, both Fg. and Fg. 3 are not legal watermark mages. These expermental results show that the proposed CNN can not extract legal watermark from the raw orgnal audo. Therefore, the has ablty to extract correspondng watermark from watermarked audos, but not from unmarked audos. (a) (b) (c) Fgure. The extracted watermarks of Fg. (a-c) for Le s method. (a) (b) (c) Fgure 3. The extracted watermarks of Fg. 3(a-c) for. 5. Conclusons In ths paper, we proposed a counter-propagaton neural network (CNN) for audo watermarkng. Dfferent from the tradtonal methods, the watermark s embedded n the synapses of CNN nstead of the orgnal audo. Therefore, the qualty of the watermarked audo s almost same as the orgnal audo. The proposed CNN does not need the orgnal audo to extract the watermark. In addton, because of the watermark s stored n the synapses, most of the attacks could not degrade the qualty of the extracted watermark mage. Thus, the proposed CNN has capablty to resst varous attacks. The watermark embeddng procedure and extractng procedure s ntegrated nto the proposed CNN. Therefore, the s smple than tradtonal methods. The expermental results show that the has good capabltes of robustness, mperceptblty and authentcty. IEEE Int. Conf. on Networkng, Sensng and Control, pp , 005. [3] Cvejc, N, Seppanen, T., Increasng robustness of LSB audo steganography usng a novel embeddng method, Pro. of the IEEE Int. Conf. on Infor. Tech.: Codng and Computng, Vol, pp ,.004. [4] Hujuan Yang, Patra, J.C., Chan, C.W., An artfcal neural network-based scheme for robust watermarkng of audo sgnals, Proc. of the IEEE Int.l Conf. on Acou., Spe., and Sg., Vol, pp , 00. [5] Fredrc M. Ham and Ivca Kostanc, Prncples of Neurocomputng for Scence & Engneerng, McGraw-Hll, Sngapore, 00. [6] Jong Won Seok and Jn Woo Hong, Audo watermarkng for copyrght protecton of dgtal audo data, Elec. Let., Vol 37, pp. 60-6, 00. [7] Sang-Kwang Lee and Yo-Sung Ho, Dgtal Audo Watermarkng n the Cepstrum Doman, Proc. of the IEEE Trans. on Con. Elect., Vol 46, pp , Aug [8] Wen-Nung Le and L-Chun Chang, "Robust and hgh-qualty tme-doman audo watermarkng subject to psychoacoustc maskng," Proc.of the IEEE nt. Symp. on Crc. and Sys., pp.45-48, 00. Acknowledgement Ths work was supported by the Natonal Scence Councl, Tawan, R.O.C. under Grants NSC 9-3-E References [] Arttameeyanant, P., Kumhom, P., Chamnongtha, K., "Audo watermarkng for Internet," Proc. of the IEEE Int. Conf. on Indust. Tech., Vol, pp , 00. [] Chuan-Yu Chang, Sheng-Jyun Su, "The applcaton of a full counterpropagaton neural network to mage watermarkng," Proc. of the