Robust Video Watermarking Using Image Normalization, Motion Vector and Perceptual Information

Transcription

1 Robust Vdeo Watermarkng Usng Image ormalzaton, Moton Vector and Perceptual Informaton Cedllo-Hernández Antono 1, Cedllo-Hernández Manuel 1, akano-myatake Marko 1, García-Vázquez Mreya S. 2 1 Postgraduate Secton of Mechancal and Electrcal Engneerng School, atonal Polytechnc Insttute of Mexco, Mexco Cty, Mexco 2 Research and Development of Dgtal Technology Center, atonal Polytechnc Insttute of Mexco, Tjuana, Mexco Abstract Ths paper proposes a vdeo watermarkng algorthm robust aganst geometrc dstortons, several attacks of sgnal processng and ntentonal common attacks for vdeo. Image normalzaton s used to get geometrc nvarant feature of vdeo frames. Watermark embeddng and detecton process are carred out n the Dscrete Cosne Transform (DCT) doman. Watermark energy s computed adaptvely usng perceptual nformaton and moton vectors, to get major Watermark mperceptblty and robustness. Watermark Imperceptblty s evaluated by conventonal PSR and perceptual vdeo qualty measurement, takng suffcently good vsual qualty. Computer smulaton results show the watermark robustness to common sgnal dstortons such as contamnaton by nose, JPEG compresson, geometrcal dstortons and vdeo common ntentonal attacks: frame droppng, frame swappng and frame averagng, among others. Keywords: Vdeo Watermarkng, Moton Vectors, Image ormalzaton, Block Classfcaton, Perceptual Sensblty 1 Introducton Hgh speed computer networks, the Internet and the World Wde Web have revolutonzed the way n whch dgtal data s dstrbuted. The wdespread and easy accesses to multmeda contents and the possblty to make unlmted copy wthout loss of consderable fdelty arouse the need of dgtal rghts management. Dgtal watermarkng s consdered as a technology that can serve ths purpose. A large number of watermarkng schemes have been proposed to hde copyrght marks and other nformaton n dgtal mages, vdeo, audo and other multmeda objects [1]. Usually watermarkng algorthms for stll mages are not effcent when these are used n vdeo sequences, because they are not consdered the temporal redundancy of vdeo sgnal and common attacks to the vdeo sgnals [2]. In the case of a watermarkng system for copyrght protecton, the embedded watermark should be mperceptble and robust aganst common attacks such as croppng, contamnaton by nose, flterng and compresson [3]. In addton to the requrement of mperceptblty and robustness technques, furthermore the vdeo watermarkng must satsfy the followng requrements: a blnd detecton,.e. the detecton process does not requre the orgnal vdeo sgnal and the conservaton of fle sze after the nserton of the watermark. Due to the redundancy exstng n the vdeo sequences, some ntentonal attacks, such as frame droppng, frame swappng and frame averagng, that try to destroy the embedded watermark should be consdered n desgn of vdeo watermarkng algorthms [3]. In ths paper, we propose a vdeo watermarkng algorthm based on mage normalzaton, n whch a watermark pattern s normalzed usng the same geometrc factors obtaned n the mage normalzaton. The proposed algorthm uses three crtera based on Human Vsual System (HVS) to nsert a robust watermark, preservng ther mperceptblty. The frst crteron s based on the senstvty of the HVS to dfferent basc color channels, the second one s based on spatal defcency of HVS, whch s determned usng the texture and edge maskng proposed by [4], and the last crteron s based on tracng defcency of the HVS n the regons wth hgh moton speed n vdeo sequences, whch s determned usng moton vector. The computer smulaton results show the watermark mperceptblty and the robustness aganst common sgnal processng, geometrcal dstortons and some ntentonal attacks to the vdeo sequence. The watermark mperceptblty s measured usng the Peak Sgnal ose Raton (PSR) and a qualty assessment based on HVS proposed by [5]. The rest of the paper s organzed as follows: In Secton 2, the proposed system s descrbed n detal and the evaluaton results of the proposed system are shown n Secton 3. Fnally Secton 4 provdes some conclusons. 2 Proposed system The proposed vdeo watermarkng system conssts of several procedures, such as mage normalzaton, block classfcaton usng the perceptual nformaton based on HVS, watermark embeddng and extracton. In ths secton, each procedure wll be descrbed.

2 2.1 Image normalzaton The normalzaton procedure of a mage f(x,y) conssts of the followng steps [6]: 1) Center the mage f(x,y), through the Affne Transformaton as gven by: x y a a x = A d y 1 0 where matrx A d = and the vector 1 d = wth 0 1 d2 m10 m01 d1 =, d2 m = 00 m (2) 00 where m 10,m 01 and m 00 are the moments of f(x,y), and d 1, d 2 are the densty center of f(x,y) [7]. Ths step elmnates the translaton effect by assgnng the center of the normalzed mage at the densty center of the mage. The resultng centered mage s denoted as f 1 (x,y). 2) Apply a shearng transform to f 1 (x,y) n the x-drecton 1 usng the matrx β A x =. The resultng mage s denoted 0 1 by f 2 (x,y). Ths step elmnates shearng effect n the x- drecton. (1) (a) Fgure 1 (a) Orgnal mage (b) ormalzed verson from (a). These crtera are based on the less senstvty of the HVS to the blue channel and the detal regons such as texture regon, and also t s less ablty to track a regon wth hgh speed moton. Accordng to the human eye structure, the retna contans two types of photoreceptors, rods and cones. The last one s dvded n 3, each senstve to the three basc colors there are three types of cones that react wth the three basc colors: Red, Green and Blue. The number of blue-senstve cones s 30 tmes less than the number of cones senstve to the other two colors [10]. Fgure 2 shows the fracton of lght absorbed by each of three types of cones, here R, G, B represents the cones senstve to red, green and blue, respectvely. Ths fgure shows that the HVS s less senstve to blue channel than the other two basc color channels (Red and Green). (b) 3) Apply a shearng transform to f 2 (x,y) n the y-drecton 1 0 usng the matrx A y =. The resultng mage s denoted γ 1 by f 3 (x,y). Ths step elmnates shearng effect n the y- drecton. 4) Scale f 3 (x,y) n both x and y drectons wth the matrx α 0 A s = and the resultng mage s denoted by f 4 (x,y). Ths 0 δ step elmnates the scalng effect by forcng the normalzed mage to a standard sze. The fnal mage f 4 (x,y) s the normalzed verson. It s mportant to denote that each step n the normalzaton procedure s nvertble, whch allows us to convert the normalzed mage back to ts orgnal verson. The determnaton of transformaton parameters β, γ, α and δ, assocated wth the transforms A x, A y, and A s are shown n detal n [9]. In the fgure 1, an example of the orgnal mage and the normalzed verson s shown. 2.2 Perceptual Informaton The proposed system uses three crtera, employed prevously n [8], to embed an mperceptble and robust watermark n a vdeo sgnal. Fgure 2 Senstvty of the three types of cones R (red), G (green) and B (blue). [13] The proposed algorthm embeds the watermark n the blue channel, takng advantage of the weakness of HVS. The blue channel of each vdeo frame s dvded nto blocks wth 8x8 pxels and then the 2D Dscrete Cosne Transform (DCT) s appled n each block. In the DCT doman, each block s classfed nto three categores: plan block, edge block and texture block accordng to the algorthm proposed n [4]. Fgure 3 shows the result of applyng the block classfcaton algorthm to a vdeo frame.

3 (a) Fgure 3 Block classfcaton (a) vdeo frame and (b) classfcaton of blocks, black blocks are plan, gray blocks are textures and whte blocks are edges, respectvely. (b) embeddng energy to the vdeo frame s determned expermentally usng 10 vdeo sequences, whch s shown n Table 1. In Table 1, B 8 s the block of 8x8 pxels n vdeo frames and MB MOTIO s the macro-block wth hgh-speed moton, each macro-block contans 4 blocks B 8. Ths assgnaton of the watermark embeddng energy s appled to the normalzed vdeo frames generated n the mage normalzaton process. Fgure 5 shows an example of the classfcaton of blocks together wth the watermark embeddng energy determned usng the crtera mentoned above. The last crteron s based on defcency of HVS to follow regons wth hgh-speed moton. To classfy regons of vdeo frame by moton speed, the moton compensaton predcton, whch s a powerful tool to reduce temporal redundancy n MPEG codng, can be used. The macro-blocks (MB) wth larger moton vector are classfed as regons of hgh-speed movement, n whch a watermark wth greater energy can be embedded wthout causng degradaton n the vdeo sgnal. The magntude of the moton vector s calculated usng equaton (3). Mmv mvh + mvv, = 1... = (3) 2 2 MB Where mvh 2,mvv 2 are the horzontal and vertcal components of moton vector of the th macro-block and MB s the total number of macro-blocks. To determne the regon wth a hgh-speed moton, we ntroduce a threshold value Th_mv and then each macro-block s classfed as hghspeed moton block and low-speed moton block (or wthout movement) as follows: If If Mmv < Th_mv then block are low-speed Mmv > Th_mv then block are hgh-speed Fgure 4 (a), (b)two consecutve frames of vdeo and (c) and (d) moton vectors before and after classfcaton Table 1 Watermark embeddng energy Where threshold Th_mv s calculated by equaton (4) 1 MB Th _ mv = Mmv (4) MB = 1 B B 8 8 Frame MB MOTIO B 8 Bplan B8 Bedge B8 Btex Fgure 4 shows two consecutve vdeo frames (Fgure 4 (a) and (b)) and the moton vectors before and after classfcaton (Fgure 4 (c) and (d), respectvely), the macro-blocks wth arrows of some orentaton are classfed as hgh-speed blocks and blocks wth black pont are consdered as low-speed blocks (or wthout movement). 2.3 Adequate watermark energy assgnment Combnng the last two crtera: the classfcaton of blocks (8x8) usng DCT coeffcents and the classfcaton of macro-blocks (16x16) usng moton vector, the watermark 2.4 Watermark embeddng process The watermark generaton and embeddng process are descrbed as follows: 1) Apply the normalzaton procedure to the orgnal mage to get the normalzed mage. 2) Dvde the normalzed mage n blocks of 8x8 pxels and get the watermark energy for every block mentoned n 2.3.

4 The result of ths operaton s a watermark embeddng energy. Iw = Io + ( WP α2) where Io and I w are orgnal and watermarked mage, and WP s watermark pattern generated by step 9. (5) The whole procedure s equvalent to embeddng the watermark nto the DCT doman of the normalzed mage. The adequate watermark embeddng energy allows embeddng strong watermark wthout causng any vsual dstorton to watermarked mage. The fgure 6 shows the watermark generaton and embeddng procedures. Fgure 5 An example of the embeddng watermark energy to each block 3) Generate the 2-D pseudo-random pattern R of the same sze of orgnal mage wth any key. Because ths pattern s only used as the support of the watermark pattern, any key can be used and t s not necessary to save. 4) Generate a watermark vector W [ w1, w2,... w n ] user s secret key, where w = { 1, 1}, =1..n. = usng a 5) Create a mask mage M, whch s a bnary mage, takng 1s wthn normalzed mage area and 0s elsewhere, to generate masked pseudo-random pattern MR wth same scale and rotaton factors as these of the normalzed mage. 6) Watermark vector W s multpled by a watermark energy vector determned by the prevous secton. 7) Dvde the watermark vector W nto groups of L elements, where L must be one number from 1 to 22 (number of coeffcents that compose the mddle frequency range n DCT doman), thus, for example, f L = 5 and sze of W s 500, the number of groups = 500 / 5 = ) The coeffcents n mddle frequency range of each block are replaced wth L elements of each group of watermark sequence. Then apply the IDCT to each watermarked block to get watermarked pattern MR. W 9) Apply the nverse normalzaton to the watermarked pattern MRW to get watermarked pattern WP wth same sze as the cover mage. 10) WP s embedded nto the orgnal mage addtvely wth a gan factor α 2. Ths produces the watermarked mage. Fgure 6 Watermark embeddng process 2.5 Watermark Extracton The process of the watermark detecton s as follows: 1) Apply the mage normalzaton procedure to watermarked mage to get the normalzed watermarked mage. 2) Apply the DCT to each block of the normalzed watermarked mage, the mddle range of the DCT coeffcents CW are extracted. From CW the watermark vector W ˆ s extracted by (6). [ w1 w2 w 1 w] sgn( CW ) Wˆ =,,, wk = k where w k s the extracted watermark sequence (L bts) from k-th block and sgn s a sgn functon. (6)

5 3 Expermental Results To evaluate the proposed system, we used 20 vdeo sequences wth YUV-CIF format at 30 FPS. All vdeo data have at least 150 frames whch are avalable n [11]. The proposed system s evaluated from the watermark mperceptblty and robustness ponts of vew. 3.1 Watermark mperceptblty We evaluate watermark mperceptblty of the proposed system usng numercal evaluaton: Peak Sgnal ose Rato (PSR) and perceptual objectve evaluaton proposed n [5]. Fgure 7 shows the PSR value calculated between orgnal and watermarked frames n the proposed method. Perceptual qualty assessment proposed n [5] evaluates vdeo qualty n perceptual manner, whch gves the qualty ndex calculated usng mage structure and moton vector. The qualty ndex s n range [0.0,1.0], when vdeo sequence under evaluaton s numercally dentcal wth ts orgnal verson, ts qualty ndex s 1.0. The qualty ndex of the watermarked vdeo sequence generated by the proposed algorthm s approxmately 0.96, whch means that degradaton of the watermarked vdeo qualty s mnmum by HVS. droppng s drop some frames from vdeo sequence, frame swappng s nterchange two frames and frame averagng s generate one frame takng the average of some consecutve frames. Due to that watermark sequence s embedded through temporal vdeo frames n the proposed algorthm; embedded watermark s robust aganst these types of attacks. Table 3 shows the performance comparson of our proposed system and the recently reported system by Soumk whch present an nvestgaton smlar to our proposal because t consders the content of vdeo as a sequence of mages and the watermark s nserted frame by frame wth an nvsble watermarkng scheme, further extracton of the watermark s blnd and s reported as a method extremely approprate n areas such as copyrght and fngerprntng [12]. Table 2. The embedded watermark sgnal s suffcently robust to geometrcal dstortons and common sgnal processng.. Attack Watermark frame attack BER Geometrc Rotaton Aspect Rato (1:2) Gaussan nose Fgure 7 Peak Sgnal to ose Rato of the proposed method. Impulsve nose Watermark Robustness To evaluate watermark robustness of the proposed algorthm, the watermarked vdeo sequences are attacked by some common sgnal and mage processng, such as codfcaton rate change, nose contamnaton by mpulsve nose and Gaussan nose, geometrcal dstortons, frame droppng, frame swappng and frame averagng. The results are shown n Table 2, n whch the bt-error-rate (BER) of the extracted watermark bt sequence respect to the embedded one s shown. In all cases the values correspond to the average values usng the 20 vdeo sequences wth YUV-CIF format. Frame droppng, frame swappng and frame averagng are ntentonal attacks for vdeo sequence. Frame MPEG Codfcaton (50% qualty) 4 Conclusons In ths paper, we proposed a vdeo watermarkng algorthm based on mage normalzaton, n whch watermark embeddng and detecton process are carred out n the

6 Dscrete Cosne Transform (DCT) doman. The watermark extracton has done blndly.e., nether the watermark nor the orgnal vdeo s needed at the tme of the watermark extracton. The proposed algorthm uses three crtera based on Human Vsual System (HVS) to embed a robust watermark, preservng ther mperceptblty. These crtera are based on the senstvty of the HVS to dfferent basc color channels, the texture and edge maskng classfcaton and the estmaton of the moton vectors n vdeo sequences. The computer smulaton results show the watermark mperceptblty and the robustness of the scheme aganst common sgnal processng, geometrcal dstortons and some ntentonal attacks to the vdeo sequence. Table 3. Robustness comparson between the proposed vdeo watermarkng and Soumk method s [12] Attack Method proposed Soumk method s [12] Geometrc Rotaton Detected - Aspect Rato Detected - Gaussan nose Detected - Impulsve nose Detected - MPEG Codfcaton Detected - Frame Droppng Detected Detected Frame Swappng Detected Detected Frame Averagng Detected Detected 5 References [1] Sourav Bhattacharya, T. Chattopadhyay and Arpan Pal, A Survey on Dfferent Vdeo Watermarkng Technques and Comparatve Analyss wth Reference to H.264/AVC. IEEE 10th Internatonal Symposum on Consumer Electroncs, June 2006, pp.1-6. Elsever Sgnal Processng: Image Communcaton, vol. 19, pp , 2004 [6] M. Cedllo-Hernández, M. akano-myatake and H. Perez-Meana, Robust Watermarkng Technque based on Image ormalzaton (Spansh), Revsta Facultad de Ingenería Unversdad de Antoqua, no. 52, pp , Marzo [7] M. K. Hu, Vsual Pattern Recognton by Moment Invarants, IRE Trans. on Informaton Theory, vol. 8, pp , [8] A. Cedllo, M. akano, H. Perez and L. Rojas, Watermarkng Technque for MPEG Vdeo usng Vsual Sensblty and Moton Vector (Spansh), Revsta Informacón Tecnología, vol. 19, no. 2, pp , [9] P. Dong, J. B. Brankov,. P. Galatsanos, Y. Yang and F. Davone, Dgtal Watermarkng robust to geometrc dstortons,, IEEE Trans. on Image Processng, vol. 14, no. 12, pp , [10] K. Sayood, Introducton to Data Compresson, 2nd Edton, Morgan Kaufmann Publshers, [11] [12] Soumk Das, Pradosh Bandyopadhyay, Shauvk Paul, Chaudhur Atal and Monalsa Banerjee, An Invsble Color Watermarkng Framework for Uncompressed Vdeo Authentcaton, Internatonal Journal of Computer Applcatons, vol. 1, no. 11, pp , February [13] Font-Color-Is-Best-For-Eyes [2] M. Swanson, B. Zhu y A. Tewfk, Mult-resoluton scene-based vdeo watermarkng usng perceptual models, IEEE J. Select areas Communcaton, vol. 16, no. 4, pp , May [3] Wolfgang, R.B, C.I. Podlchuk y E.J. Delp, Perceptual watermarks for dgtal mages and vdeo, Proceedng IEEE, vol. 87, no. 7, pp , [4] H.Y. Tong an d A.. Venetsanopoulos, A Perceptual model for JPEG applcatons based on block classfcaton, texture maskng and lumnance maskng, Int. Conf. on Image Processng (ICIP), vol. 3, pp , [5] Z. Wang, L. Lu and A. C. Bovk, Vdeo qualty assessment based on structural dstorton measurement,