Protection of Real and Artwork Human Objects based on a Chaotic Moments Modulation Method

Transcription

1 1 Protecton of Real and Artwork Human Objects based on a Chaotc Moments Modulaton Method Klms Ntalans 1, Paraskev Tzouvel 1, Stefanos Kollas 1 and Athanasos Drgkas 1 Natonal Techncal Unversty of Athens, Electrcal and Computer Engneerng Department 9, Heroon Polytechnou str., Zografou 15773, Athens, Greece Net Meda Lab, NCSR Demokrtos, Athens, Greece kntal@mage.ntua.gr Abstract: Content analyss technologes gve more and more emphass on multmeda semantcs. However most watermarkng systems are frame-orented and do not focus on the protecton of semantc regons. As a result, they fal to protect semantc content, especally n case of the copy-paste attack. In ths framework, a novel unsupervsed semantc regon watermark encodng scheme s proposed. The proposed scheme s appled to real and artwork human objects, localzed by two dfferent face and body detecton methods. Next, an nvarant method s desgned, based on Hu moments, for properly encodng the watermark nformaton nto each semantc regon, usng a chaotc pseudo-random number generator. Fnally, experments are carred out, to llustrate the advantages of the proposed scheme, such as: a robustness to RST, copy-paste and other attacks, and b low overhead transmsson. Keywords: Semantc regon protecton, artwork objects, Hu moments, Chaotc pseudo-random number generator. 1. Introducton Copyrght protecton of dgtal mages, vdeo and artworks s stll an urgent ssue of ownershp dentfcaton. Several watermarkng technques have been proposed n lterature [1]-[11]. Among them some mlestone methods nclude: a [9], who state that a watermark should be constructed as an..d. Gaussan random vector and be mperceptbly nserted n a spread-spectrum-lke fashon, nto the perceptually most sgnfcant spectral components of the data. They report that the use of Gaussan nose ensures strong reslence to multple-document, or collusonal attacks. b In [10], a hybrd method s proposed, whch combnes two dfferent watermark-embeddng strateges for nsertng nformaton n the DCT coeffcents of 8 8 blocks of the host vdeo. c Addtonally, n [11] quantzaton s used for watermark embeddng n the low frequences and spread spectrum watermarkng s appled to the hgh frequences, provdng a way to maxmze robustness to dfferent types of attacks. Most of the abovementoned approaches rely on the nserton of pseudorandom nose nto the orgnal data. Generally, the resultng alteratons do not change the essental propertes of the data and cannot be perceved by the HVS. However the majorty of them are not resstant enough to geometrc attacks, such as rotaton, scalng, translaton and shearng. Several researchers [1]-[18] have tred to overcome ths neffcency by desgnng watermarkng technques resstant to geometrc attacks. Some of them are based on the nvarant property of the Fourer transform. Others use moment-based mage normalzaton [19], wth a standard sze and orentaton or other normalzaton technques [0]. In most of the aforementoned technques the watermark s a random sequence of bts [10] and t s retreved by subtractng the orgnal from the canddate mage and choosng an expermental threshold value to determne when the cross-correlaton coeffcent denotes a watermarked mage or not. On the other hand, another defcency of the majorty of the exstng technques s that they are frame-based and thus semantc regons such as humans, buldngs, cars etc., are not consdered. These regons may need better protecton or can be the only regons that need protecton, dependng on the specfc applcaton. Furthermore, typcal watermark detecton modules fal to extract watermark nformaton n case of copyng and pastng a semantc regon copy-paste attack, due to complete loss of synchronzaton. Even though a lmted number of regon watermarkng schemes has also been proposed [1]-[4], the lterature stll lacks effcent algorthms for content authentcaton especally n case of the copy-paste attack. At the same tme, multmeda analyss technologes gve more and more mportance to semantc content and n several applcatons semantc regons, and especally humans, are addressed as ndependent vdeo objects [5] and thus should be ndependently protected. Towards ths drecton the proposed nnovatve system s specfcally desgned to provde geometrcally resstant copyrght protecton of semantc content n two cases: n case of generc real world human objects and n case of artwork human objects, exstng n Byzantne conography. To acheve ths goal, n these cases a human object detecton module s requred both n watermark encodng and durng authentcaton. After object detecton the watermark encodng phase s actvated, where chaotc nose s properly generated and added to the detected human objects, producng the watermarked human objects. For authentcaton reasons, the watermark encodng procedure s guded by a feedback mechansm n order to satsfy a specfc equalty, formed as a weghted dfference between Hu moments of the orgnal and watermarked human objects. Durng authentcaton, ntally every receved mage/artwork passes through the human object detecton module. Then Hu moments are calculated for each detected human object, and a specfc nequalty s examned. A receved human object s copyrghted only f the nequalty s satsfed. Expermental

2 results on real sequences ndcate the advantages of the proposed scheme n cases of mxed attacks, affne dstortons and the hghly nnovatve copy-paste attack. The rest of ths paper s organzed as: n Secton we brefly descrbe Hu moment nvarant functons. In Secton 3 the human object extracton submodules are descrbed. Next, n Secton 4 the proposed watermark encodng module s dscussed whle Secton 5 focuses on the watermark decodng module. Expermental results are presented n Secton 6 to ndcate the promsng performance of the proposed system. Fnally the paper s concluded n Secton 7.. Moment Invarant Functons Geometrc moments and moment nvarants are brefly presented n ths secton. Moments and functons of moments have been utlzed as pattern features n a varety of applcatons [10], [19], [6], [7]. Geometrc transformatons have been based on a moments constructve way for extractng features, whch can provde global nformaton about -D mages. Moment nvarants nclude: a moments that are nvarant under change of sze, translaton, and rotaton only and b moments that are nvarant under all prevous changes as well as reflecton. In ths paper, Hu moments are used durng the watermark encodng phase of semantc objects. Tradtonally, moment nvarants are computed based both on the shape boundary of the area and on ts nteror. Hu frst ntroduced [0] the mathematcal foundaton of -D moment nvarants, based on methods of algebrac nvarants and demonstrated ther applcaton to shape recognton. Hu s method s based on nonlnear combnatons of nd and 3 rd order normalzed central moments, provdng a set of RST nvarant functons. Actually, Hu descrbed two dfferent methods for producng rotaton nvarant moments. The frst, based on prncpal axes, mght present problems when mages do not have unque prncpal axes rotatonally symmetrc. In the second method, Hu descrbed the concept of absolute moment nvarants, whch are derved through algebrac nvarants appled to the moment generatng functon, under a rotaton transformaton. The result s a set of absolute orthogonal moment nvarants, whch can be used for RST nvarant pattern dentfcaton. From the nd and 3 rd order central moments, a set of sx absolute orthogonal nvarant moments can be computed as: The 7th moment skew orthogonal nvarant s useful for dstngushng mrror mages: The frst sx of these moments are also nvarant under reflecton, whle φ 7 changes sgn. These seven moments φ 1 - φ 7 are used by the proposed method for watermark encodng. 3. Semantc Regon Extracton A very mportant subsystem of the proposed system, whch supports content authentcaton n case of the hghly nnovatve copy-paste attack, s the human objects extracton module. Snce the overall system s desgned to provde geometrcally resstant copyrght protecton of both real world and Byzantne art objects, the human object extracton module conssts of two submodules: a the real world human object extracton submodule and b the artwork object extracton submodule. The frst submodule depends on chromnance and topology modelng of face and body through Gaussan p.d.fs, whle the second s based on fundamental knowledge and essental rules for analyzng and nterpretng Byzantne artworks. These rules are descrbed n detal n the theoretcal approach of Donysos from Fourna [8], an expert n Byzantne art. In the followng subsectons both submodules are analytcally presented. A. The real world human object extracton submodule In the proposed scheme we focus on semantc content authentcaton; n ths framework, a regon s defned through segmentaton. In ths paper, human objects are selected as target regons, snce they consttute ndependent enttes n applcatons and may provde semantc nformaton about a shot or an mage. In such applcatons, t s often mportant to carefully handle and effectvely protect them. Other semantc objects can also be selected as target regons, such as buldngs, vehcles, anmals, etc. Havng selected the type of target regon, a semantc segmentaton algorthm should be ncorporated for human object extracton. In ths paper, ntally the human face s localzed and then the human body s detected usng topologcal nformaton based on the human face Fgure 1. Both modules are analytcally descrbed n the next paragraphs. Human face detecton s a topc of extensve research for several decades. Face detecton methods can be classfed as ether feature or mage based. Among feature-based methods, those usng skn color have ganed strong popularty. The advantages of skn color based methods are the fast processng and the sgnfcant robustness to geometrc varatons of face patterns. Due to these advantages, detecton of human faces s accomplshed n ths paper, by combnng key deas of the feature nvarant method proposed n [9], based on a Gaussan p.d.f. Accordng to [9], the dstrbuton of chromnance values of each block, belongng to a human face, occupes a very small regon of the colorspace. Based on ths dea, the blocks of an mage that are located nsde ths small regon can be consdered as face blocks. Let Ω f denote the face class. Then the hstogram of

3 3 chromnance values correspondng to the face class can be ntally modelled by a Gaussan p.d.f. as: 1 1 T exp x μ f x μ f f P x f 3a 1/ Σ where x = [u v] T s a 1 vector contanng the mean chromnance components u and v of an examned block, μ f s the 1 mean vector of a face class and Σ s the x varance matrx of the p.d.f.: u u, v u, v v Σ 3b where σ u s the varance of the chromnance component u, σ v s the varance of the chromnance component v and σ u,v corresponds to the covarance between u and v. Parameters μ f and Σ are estmated, based on a set of several face mages, through a maxmum lkelhood approach []. Next, each block B of an mage s consdered to belong to the face class, f the respectve probablty of ts chromnance values, PxB Ω f s hgh PxB Ω f > 0.9. Then, by fusng those blocks belongng to face class Ω f, a bnary mask M s produced, contanng canddate face regons. However, mask M may also contan non-face blocks that present smlar chromnance characterstcs lke hands, legs or other parts of the human body. To confront ths problem, shape nformaton of human faces s also consdered, by usng rectangles wth certan aspect ratos [31]. In partcular, the aspect rato of face areas can be defned as R= H f /W f where H f s the heght of the head, whle W f corresponds to the face wdth. Accordng to ths approach, R was expermentally found to le wthn the nterval [ ]; consequently regons wth aspect ratos wthn ths nterval are consdered as face regons, whle the rest are dscarded. After checkng all canddate face areas and dscardng those that do not satsfy the aspect rato rule, a fnal bnary mask, say M f, s bult that contans only face areas. Detecton of the body area can be acheved usng topologcal attrbutes that relate the locatons of face and body. Intally the centre, wdth and heght of the estmated face regon, denoted as c f = [c x c y ] T, w f and h f respectvely, are computed. Human body s then localzed by means of a probablstc model, the parameters of whch are estmated accordng to c f, w f and h f. In partcular, f rb =[r x B r y B ] T s the dstance between the -th block, B, and the orgn, wth r x B and r y B the respectve x and y coordnates, the product of two ndependent 1-dmensonal Gaussan p.d.fs s used to model the locaton of human body. Thus, for each block B of an mage, a probablty PrB Ω b s assgned, expressng the degree of block B belongng to the human body class, say Ω b 1 1 exp rx B x exp ry B y x y P r B b 4 where μ x, μ y, σ x and σ y are the parameters of the human body localzaton model; these parameters are calculated based on the nformaton derved from the face detecton task, takng x y nto account the relatonshp between human face and body. In our smulatons, the parameters of the human body localzaton model are estmated wth respect to the face regon as follows [3]: x c x, y c y h f x w f,, y h f / 5 Smlarly to human face detecton, a block B belongs to the body class Ω b, f the respectve probablty, PrB Ω b, s hgh, usng a smlar threshold as n the face detecton case. The computed face and body masks can be properly used to extract human objects [3]. a c b Fg. 1: Human Vdeo Object Extracton Method: a Intal Image b Object Mask c Object Extracton Ths algorthm s an effcent method for fndng face locatons n complex backgrounds, when the sze of faces s unknown. It can be used for a wde range of face szes. The performance of the algorthm s based on the dstrbuton of chromnance values correspondng to human faces, provdng 9% segmentaton success. B. The artwork fgure extracton submodule Panters of Byzantne artworks follow the specfc nstructons that Donysos from Fourna had recorded for pantng holy fgures. Accordng to these nstructons, ntally a panter separates the pantng area nto seven semantc segments of equal sze, each of whch has specfc characterstcs that can make the fgure dstngushable. An example s presented n Fg., where the standng holy fgure of Jesus Chrst s presented. Startng from top to bottom, the frst segment contans the head of the holy fgure whch s further separated nto 4 equal smaller semantc parts P 6 4 H The second segment, also wth same heght P, contans the part from neck to thorax whle the thrd segment contans the part from thorax to elbow and wast, whch always lay at the same heght. Next, n the fourth segment the abdomnal area s usually depcted, whle the ffth segment contans the area from legs untl the knees. The sxth segment contans from

4 4 ankle to foot and fnally the feet of the fgure are located at the seventh segment. the orgnal mages produces the fnal mages, whch contan the extracted heads. The pctures n Fg. 3a show, from left to rght frst, the stages for the head extracton for one mage extracton from halo locaton, threshold calculaton, medan flterng, and fnal extracton and n Fg. 3b the extracted heads for nne such mages are shown. Fg. 3a: Head extracton for one Holy fgure Fg. : Metrc rules of Byzantne conography Accordng to Donysos from Fourna, the head of a sant should be surrounded by a halo, a crcle that sgnfes the Holy Sprt see Fg.. In order to pant the halo, the panter draws a crcle, centered at the mddle pont of the nose wth a radus R =.5 H or accordng to 6: P = 1.6 R 7 For the halo dentfcaton, we use the Hough transform, [33] by followng the next steps: 1. Quantzaton of the parameter space wth regard to the parameters a and b of the Hough transform.. Assgn an accumulator to each cell n the parameter space and ntalze all accumulators Ma, b to zero. 3. Compute the gradent drecton θx, y and magntude Gx, y for all the edge ponts n the mage. 4. For each edge pont Gx, y ncrement all ponts n the accumulator array Ma, b along the lne: b = a tanθ - x tanθ + y 5. Fnd the local maxma n the accumulator array and determne the center of the crcles. After the halo crcle has been dentfed, we estmate two thresholds by drawng two concentrc crcles nscrbed nto the halo. From the small crcle we estmate the average ntensty value for the actual head and from the rng between the greater crcle and the halo we estmate the ntensty value for the halo. By choosng a threshold between these two values, we segment the area n the rng between the two crcles n two regons, head and halo. Fnally, we apply a medan flter wth approprate sze to the segmented mage n order to produce masks that better solate the extracted head area. Applcaton of these masks to Fg. 3b: Extracted heads of several Holy fgures Then, the heght of the mage Y s gven accordng to Donysos manual as: P P Y A A Y 8 where A s the area n whch the holy fgure s llustrated and P the heght of each semantc part see Fg.. Then the body area of the fgure s gven by B = A P B = Y.4 R 9 Havng the values of the homocentrc crcles nscrbed nto the man halo crcle, the ntensty values of the background of the mage s known. So, the extracton of the body area s acheved by croppng the proper area wth heght B and extract the background part as t s depcted n Fg. 4.

5 5 Fg. 4: Extracted Holy fgures Fg 5: Block dagram of the encodng module 4. The Watermark Encodng Module Let us assume that human object O has been extracted from an mage or frame, usng the object extracton modules descrbed n Secton 3. Intally, Hu moments of human object O are computed [0], provdng an nvarant feature of an object. Tradtonally, moment nvarants are computed based both on the shape boundary of the area and ts nteror object. Hu frst ntroduced the mathematcal foundaton of - D moment nvarants, based on methods of algebrac nvarants and demonstrated ther applcaton to shape recognton. Hu s method s based on nonlnear combnatons of nd and 3 rd order normalzed central moments, provdng a set of absolute orthogonal moment nvarants, whch can be used for RST nvarant pattern dentfcaton. Hu derved seven functons from regular moments, whch are rotaton, scalng and translaton nvarant. In [3], Hu s moment nvarant functons are ncorporated and the watermark s embedded by modfyng the moment values of the mage. In ths mplementaton, exhaustve search should be performed n order to determne the embeddng strength. The method that s proposed n [3] provdes an nvarant watermark n both geometrc and sgnal processng attacks based on nvarant of moments. Hu moments are seven nvarant values computed from central moments through order three, and are ndependent of object translaton, scale and orentaton. Let Φ= [φ 1, φ, φ 3, φ 4, φ 5, φ 6, φ 7 ] Τ be a vector contanng the Hu moments of O. In ths paper, the watermark nformaton s encoded nto the nvarant moments of the orgnal human object. To accomplsh ths, let us defne the followng functon: 7 x f X, w 10 1 where X s a vector contanng the φ values of an object, Φ contans the φ nvarants of object O and w are weghts that put dfferent emphass to dfferent nvarants. Each of the weghts w receves a value wthn a specfc nterval, based on the output of a chaotc random number generator. In partcular chaotc functons, frst studed n the 1960's, present numerous nterestng propertes that can be used by modern cryptographc and watermarkng schemes. For example the teratve values generated from such

6 6 functons are completely random n nature, although they are lmted between some bounds. The teratve values are never seen to converge after any number of teratons. However the most fascnatng aspect of these functons s ther extreme senstvty to ntal condtons that make chaotc functons very mportant for applcatons n cryptography. One of the smplest chaotc functons that are ncorporated n our work s the logstc map. In partcular, the logstc functon s ncorporated, as core component, n a chaotc pseudo-random number generator C-PRNG [34]. The procedure s trggered and guded by a secret 56-bt key that s splt nto 3 8-bt sesson keys k 0, k 1,, k 31. Two successve sesson keys k n and k n+1 are used to regulate the ntal condtons of the chaotc map n each teraton. The robustness of the system s further renforced by a feedback mechansm, whch leads to acyclc behavor, so that the next value to be produced depends on the key and the current value. In partcular the frst 7 output values of C-PRNG are lnearly mapped to the followng ntervals: [ ] for w 1, [ ] for w, [1 1.5] for w 3, [0.75 1] for w 4 and w 5, and [ ] for w 6 and w 7. These ntervals have been expermentally estmated based on the mportance and robustness of each of the φ nvarants. Then watermark encodng s acheved by enforcng the followng condton: 7 * * * 1 f, w N 11 where Φ* s the moments vector of the watermarked human object O* and N* s a target value also properly determned by the C-PRNG, takng nto consderaton a tolerable content dstorton. N* value expresses the weghted dfference among the φ nvarants of the orgnal and the watermarked human objects. The greater the value s, the larger perturbaton should be added to the orgnal vdeo object and the hgher vsual dstorton would be ntroduced. Ths s acheved by generatng a perturbaton regon ΔΟ of the same sze as O such that, when ΔΟ s added to the orgnal human object O, t produces a regon O* = O + β ΔΟ 1 Fg 6: Block Dagram of the decodng module that satsfes Eq. 11. Here, β s a parameter that controls the dstorton ntroduced to O by ΔΟ. C-PRNG generates values untl mask ΔΟ s fully flled. After generatng all senstve parameters of the watermark encodng module, a proper O* s teratvely produced usng Eqs. 11 and 1. In ths way, the watermark nformaton s encoded nto the φ values of O producng O*. An overvew of the proposed watermark encodng module s presented n Fg The Decodng Module The decodng module s responsble for detectng copyrghted human objects. The decodng procedure s splt nto two phases Fg. 6. Durng the frst phase, the receved mage passes through the human object extracton module descrbed n Secton 3. Durng the second phase each human object undergoes an authentcaton test to check whether t s copyrghted or not. In partcular let us consder the followng sets of objects and respectve φ nvarants: a O, Φ for the orgnal human object, b O*, Φ* for the watermarked human object and c O, Φ for a canddate copyrghted human object. Then O s declared authentc f: * f, f, 13 where fφ*, Φ s gven by Eq.15, whle fφ, Φ s gven by: 7 f, w N ' 14 1 Then Eq. 13 becomes 7 * * Nd N N w 15 1 where ε s an expermentally determned, case-specfc margn of error and w are the weghts.

7 7 Two observatons need to be stressed at ths pont. It s advantageous that the decoder does not need the orgnal mage. It only needs w, Φ, Φ* and the margn of error ε. Secondly, snce the decoder only checks the valdty of Eq. 15 for the receved human object, the resultng watermarkng scheme answers a yes/no,.e. copyrghted or not queston. As a consequence, ths watermarkng scheme belongs to the famly of algorthms of 1-bt capacty. Now n order to determne ε, we should frst observe that Eq. 15, delmts a normalzed margn of error between Φ and Φ*. Ths margn depends on the severty of the attack,.e., the more severe the attack, the larger the value of N d wll be. Thus, ts value should be properly selected so as to keep false reject and false accept rates as low as possble deally zero. More specfcally, the value of ε s not heurstcally set, but depends on the content of each dstnct human object. In partcular, each watermarked human object, O*, undergoes a sequence of plan e.g. compresson, flterng etc. and mxed attacks e.g. croppng and flterng, nose addton and compresson of ncreasng strength. The strength of the attack ncreases untl, ether the SNR falls below a predetermned value, or a subjectve crteron s satsfed. In the followng the subjectve crteron s selected, whch s related to the content s vsual qualty. Accordng to ths crteron and for each attack, when the qualty of the human object s content s consdered unacceptable for the majorty of evaluators, an upper level of attack, say A h, s set. Ths upper level of attack can also be automatcally determned based on SNR, snce a mnmum value of SNR can be defned before any attack s performed. Let us now defne an operator p. that performs attack to O* reachng upper level A and producng an object O *: h * * p O, Ah O, 1,,..., M 16 Then for each O *, N d s calculated accordng to Eq. 15. By gatherng N d values, a vector s produced: N N, N,..., N 17 d d1 d d M Then the margn of error s determned as: max N d 18 Snce ε s the maxmum value of N d, t s guaranteed that human objects should be vsually unacceptable n order to deceve the watermark decoder. 6. Expermental Results Several experments were performed to examne the advantages and open ssues of the proposed method. Frstly, face and body detecton was performed on dfferent mages, both real world and artstc. The followng expermental results concern the real world object of Fgure 1c and the mddle artwork holy fgure of Fgure 4. After objects extracton, the watermark was encoded to each object and the decodng module was tested under a wde class of geometrc dstortons, copy-paste and mxed attacks. When an attack of specfc type was performed to each one of the watermarked objects real world and artwork, t led to SNR reducton that was proportonal to the severty of the attack. Frstly we examned JPEG compresson for dfferent qualty factors n the range of 10 to 90. Result sets N*, SNR, N d are provded n the frst group of rows of Table I. It can be observed that N d changes rapdly for SNR < 9.6 db. Furthermore, the subjectve vsual qualty s not acceptable for SNR < 10 db for both categores of human objects. Smlar behavors can be observed n the cases of Gaussan nose for SNR < 11 db usng dfferent means and devatons and medan flterng for SNR < 10 db changng the flter sze. By summarzng the results n Table I, t can be observed that n most cases the proposed system can successfully authentcate watermarked content. Fg 7: Copy-paste attack. a Watermarked human object b Modfed watermarked human object n new content In the followng, we also llustrate the ablty of the method to protect watermarked content n case of the very nnovatve and wdespread copy-paste attack. The encodng module receves an mage whch contans a weather forecaster and provdes the watermarked human object Fg 7a. In ths case ε was automatcally set equal to 0.65 accordng to Eq. 18, so as to confront even croppng naccuracy of 6 %. It should be mentoned that, for larger ε, larger croppng naccuraces can be addressed, however, the possblty of false alarms also ncreases. Now let us assume that a malcous user ntally receves Fg. 7a and then copes, modfes croppng naccuracy of %, scalng 5%, rotaton 10 o and pastes the watermarked human object n a new content Fg. 7b. Let us also assume that the decodng module receves Fg. 7b. Intally the human object s extracted and then the decoder checks the valdty of Eq. 18. In ths case N d =0.096, a value that s smaller than ε. As a result the watermark decoder certfes that the human object of Fg. 7b s copyrghted, even though t s nserted to a completely new background. Results for dfferent percentages of croppng naccuracy are presented n the last row or Table I, both for the real and the artwork objects. As mentoned, a crucal ssue for the determnaton of ε has to do wth the accuracy of croppng. In partcular, let us assume that a malcous user crops the watermarked holy fgure object of Table II, n order to reuse t. We examne three dfferent ways that the malcous user can ncorporate n order to extract the holy fgure from the rest of the artwork Fgure 4a. In the frst case the malcous user apples rectangular croppng, whle n the second and thrd he uses lasso croppng wth dfferent accuracy. The smulaton of these attacks and results are presented n Table II. As t can be observed the croppng attack has a larger mpact on the value of N d, compared to the rest of the attacks of Table I, and for ths reason selecton of ε s manly based on these outcomes.

8 8 Table II: Croppng and holy fgure object authentcaton Cropped Object Rectangle area Lasso technque Object extracton SNR ,45 N d Conclusons The latest multmeda systems and technologes gve more and more emphass on semantc regons, ther detecton, analyss, recognton and protecton. However most of the exstng watermarkng schemes are frame-based and do not ndependently protect semantc regons. These regons wthn a frame may need better protecton, compared to the rest, semantcally ndfferent, content or can be the only regons that need protecton. Currently, typcal watermark detecton modules fal to authentcate semantc regons, due to complete loss of synchronzaton. They are only able to authentcate a frame as a whole. Thus the copy-paste attack s not addressed. In ths paper we have proposed an unsupervsed, robust to geometrc attacks and low complexty semantc objects watermarkng scheme. Two cases have been studed: the case of generc real world human objects and the case of Byzantne art objects. For the frst case ntally human objects are extracted, usng skn-tone color and shape and topology constrants that are bult nto Gaussan probablstc models. For the second case, fundamental knowledge and essental rules from the handbook of Donysos from Fourna are ncorporated, for analyzng and nterpretng Byzantne artworks. Next, a watermark s encoded to each human object by properly modfyng ts Hu moments. Fnally durng authentcaton, ntally the human objects extracton module s ncorporated and then authentcaton s performed on the detected regons. Table I: Expermental results for the real world human object of Fgure 1c and the mddle artwork holy fgure of Fgure 4. N* 0,0193 jpeg Compresson Gaussan Nose Medan Flterng Real world human object Artwork human object Qualty SNR,80 9,63 1,99 14,37 15,36 5, 10,4 13, N d 0,160 0,0141 0,0110 0,0057 0,0076 0,1840 0,016 0,0105 0,0090 0,0079 v=0, σ 1,80 1,40 1,00 0,06 0,0 1,80 1,40 1,00 0,06 0,0 SNR 9,49 11,44 13,90 17,41 3,17 10,0 1,64 15,14 19,44 8,98 N d 0,340 0,0109 0,0065 0,00 0,005 0,0174 0,0099 0,05 0,007 0,0004 [nxn] SNR 9,49 10,9 1,7 1,93 0,68 10,85 11,19 1,81 15,34 4,63 N d 0,1166 0,106 0,0970 0,0117 0,0 0,199 0,114 0,0991 0,0043 0,0043 Degrees Rotaton SNR 3,13 3,9 3,96 4,78 6, 3,49 3,63 4,78 5,56 8,14 N d 0,1490 0,1489 0,1487 0,1483 0,1480 0,149 0,1487 0,1481 0,1480 0,1477 % 0,0 0,60 1,00 1,40 1,80 0,0 0,60 1,00 1,40 1,80 Scalng SNR 3,16 3,48 4,00 4,68 6,3 3, 3,67 4,35 5,56 8,43 N d 0,0690 0,0700 0,0700 0,0710 0,0690 0,0580 0,058 0,0583 0,058 0,0586 Free croppng % N d 0,3840 0,4560 0,5090 0,600 0,660 0,35 0,4956 0,5869 0,693 0,6989 Here t should be mentoned that the authentcaton module only uses the moment values of the orgnal and watermarked human object and the moment weghts. For these reasons, both the encodng and decodng modules have low complexty. Expermental results on both real sequences and Byzantne artworks ndcate the robustness of the proposed watermarkng method under varous sgnal dstortons, mxed processng and especally the hghly nnovatve copy-paste attack. Fnally we should note that the proposed scheme reaches a very hgh performance, whch however depends on the accuracy of the human object extracton module. In a future work ths problem wll be further addressed and subobject authentcaton methods wll be proposed, so that next generaton watermarkng schemes can protect the semantc content of mages more effectvely than the exstng methods.

9 9 Acknowledgment Ths research s performed n the framework of the Greek Secretarat of Research and Technology Project MORFES: Spatotemporal Modelng of Standng, Full-Length and Dmdate Fgures n Works of Art, by Retreval of Proportons, based on the Monuments of the Foundaton of the Holy Monastery of Mount Sna, whch s co-funded by the European Socal Fund 75% and Natonal Resources 5%. References [1] J. Cox, M. L. Mller, and J. A. Bloom, Dgtal Watermarkng, San Mateo, Morgan Kaufmann, 001. [] S. Katzenbesser and F. Pettcolas, Informaton Hdng Technques for Steganography and Dgtal Watermarkng, Artech House, 001. [3] F. Pettcolas, R. Anderson, and M. Kuhn, Attacks on Copyrght Markng Systems, n Proceedngs of the nd Internatonal Workshop of Informaton Hdng, pp , [4] J. Wang, S. Lan, Y. Da, G. Lu and Z. Ren, Secure Sem-Fragle Mult-Feature Watermarkng Authentcaton Scheme, Journal of Informaton Assurance and Securty, Vol. 1, p.p , 006. [5] H.O. Altun, A. Orsdemr, G. Sharma and M.F. Bocko, Optmal Spread Spectrum Watermark Embeddng va a Multstep Feasblty Formulaton, IEEE Transactons on Image Processng, Vol. 18, No., February 009. [6] N. B, Q. Sun, D. Huang, Z. Yang and J. Huang, Robust Image Watermarkng Based on Multband Wavelets and Emprcal Mode Decomposton, IEEE Transactons on Image Processng, Vol. 16, No. 8, August 007. [7] S.P. Maty and S. Maty, Multstage Spread Spectrum Watermark Detecton Technque Usng Fuzzy Logc, Sgnal Processng Letters, Vol. 16, No. 4, pp , Aprl 009. [8] S.K. Kapotas and A.N. Skodras, Real tme data hdng by explotng the IPCM macroblocks n H.64/AVC streams, Journal of Real-Tme Image Processng, Vol. 4, No. 1, March 009. [9] I. J. Cox, J. Klan, F. T. Leghton, and T. Shamoon Secure Spread Spectrum Watermarkng for Multmeda n IEEE Transactons on Image Processng, Vol. 6, No. 1, [10] M. Alghonemy and A. H. Tewfk, Geometrc Invarance n Image Watermarkng n IEEE Transactons on Image Processng, Vol. 13, No., February 004. [11] B. Chen and G. W. Wornell, Quantzaton ndex modulaton: a class of provably good methods for dgtal watermarkng and nformaton embeddng, n IEEE Transactons on Informaton Theory, vol. 47, pp , 001. [1] C. Y. Ln, M. Wu, J. Bloom, I. Cox, M. Mller, and Y. Lu, Rotaton, scale, and translaton reslent watermarkng for mages, IEEE Trans. on Image Processng, vol. 10, pp , 001. [13] M.Wu and H. Yu, Vdeo access control va mult-level data hdng, n Proc. of the IEEE ICME, N.Y. York, 000. [14] S. Perera and T. Pun, Robust template matchng for affne resstant mage watermarks, IEEE Transactons on Image Processng, vol. 9, no. 6, 000. [15] L. Cora1, P. Nasopoulos, R. Ward and M. Pckerng, An Access Control Vdeo Watermarkng Method that s Robust to Geometrc Dstortons, Journal of Informaton Assurance and Securty, Vol., p.p , 007. [16] D. Zheng; S. Wang and J. Zhao, RST Invarant Image Watermarkng Algorthm Wth Mathematcal Modelng and Analyss of the Watermarkng Processes, IEEE Trans. on Image Processng, vol. 18, pp , May 009. [17] Y. Wang and A. Pearman Blnd MPEG- vdeo watermarkng robust aganst geometrc attacks: a set of approaches n DCT doman, IEEE Trans. on Image Processng, vol. 15, pp , June 006. [18] X.Y. Wang and C.Y. Cu, A novel mage watermarkng scheme aganst desynchronzaton attacks by SVR revson, Journal of Vsual Communcaton and Image Representaton, Elsever, No. 5, pp , July 008. [19] Y. Abu-Mostafa and D. Psalts, Image normalzaton by complex moments, n IEEE Trans. on Pattern Analyss and Machne Intellgent, vol.7, [0] M. K. Hu, Vsual pattern recognton by moment nvarants, n IEEE Trans. on Informaton Theory, vol. 8, pp , 196. [1] Ypng Chu, Yn Zhang, Sanyuan Zhang, Xuz Ye, "Regon of Interest Fragle Watermarkng for Image Authentcaton," 1 st Internatonal Mult-Symposums on Computer and Computatonal Scences, vol. 1, pp , 006. [] H. Lu and M. Stenebach, Non-Ubqutous Watermarkng for Image Authentcaton by Regon of Interest Maskng, n Proceedngs of the Pcture Codng Symposum, Portugal, 007. [3] K. Zebbche and F. Khelf, Regon-Based Watermarkng of Bometrc Images: Case Study n Fngerprnt Images, Internatonal Journal of Dgtal Multmeda Broadcastng, vol. 008, 008. [4] X. Guo and T.-G Zhuang, A Regon-Based Lossless Watermarkng Scheme for Enhancng Securty of Medcal Data, Journal of Dgtal Imagng, Sprnger, Vol., No. 1, p.p , February 009. [5] MPEG-4 Part 10, Advanced Vdeo Codng, ISO/IEC, May 0. [6] J. Wood, Invarant pattern recognton: a revew, Pattern Recognton, vol. 9, no. 1, pp. 1 17, [7] S. O. Belkasm, M. Shrdhar, and M. Ahmad, Pattern Recognton wth Moment Invarants: A Comparatve Study and New Results, Pattern Recognton Socety, Vol. 4, No. 1, pp , [8] P. Hetherngton, The Panter's Manual of Donysus of Fourna. London: Sagttarus Press, [9] G. Yang and T. S. Huang, Human Face Detecton n Complex Background, n Pattern Recognton, vol. 7, no. 1, pp , [] Papouls A., Probablty, Random Varables, and Stochastc Processes, McGraw Hll, New York, 1984.

10 10 [31] H. Wang, S-F. Chang A hghly effcent system for automatc object detecton n MPEG vdeo, n IEEE Transactons on Crcuts and Systems for Vdeo Technology, vol. 7, pp , [3] P. Tzouvel, K. Ntalans, S. Kollas, Human Semantc Object Watermarkng Based on HU Moments n Proceedngs of the IEEE Workshop on Sgnal Processng Systems, Athens, Greece, November 005. [33] Ballard D. H., Generalzng the Hough transform to detect arbtrary shapes, Pattern Recognton, Vol. 13, No., pp , [34] R. Devaney, An Introducton to Chaotc Dynamcal Systems, Redwood Cty, CA: Addson-Wesley, Authors Bographes Dr. Klms Ntalans was born n Athens, Greece, n He receved the Dploma degree and the PhD degree n electrcal and computer engneerng, both from the Natonal Techncal Unversty of Athens NTUA, Athens, Greece, n 1998 and 00 respectvely. He s the author of more than 50 scentfc artcles and a revewer of several nternatonal journals and conferences. Hs research nterests nclude 3-D mage processng, vdeo organzaton, multmeda cryptography and data hdng. Durng the last decade, Dr. Ntalans has receved przes for hs academc achevements. Hs PhD studes were supported from the Natonal Scholarshps Foundaton and the Insttute of Communcatons and Computers Systems of the NTUA. Dr. Klms Ntalans has partcpated n 1 Greek and European projects as researcher and n 4 Greek and European projects as senor researcher. Dr. Ntalans s a member of the Techncal Chamber of Greece. Dr. Paraskev Tzouvel was born n Athens, Greece. She obtaned her Dploma from School of Electrcal and Computer Engneerng of Natonal Techncal Unversty of Athens n 001 and she s currently pursung her Ph.D. degree at the Image, Vdeo, and Multmeda Systems Laboratory at the same Unversty. She s the author of more than papers and a revewer of several nternatonal journals and conferences. Her current research nterests le n the areas of mage and vdeo analyss, nformaton retreval, knowledge manpulaton, cryptography and e-learnng systems. She has been a member of the Techncal or Advsory Commttee Prof. Stefanos Kollas was born n Athens, Greece. He obtaned hs Dploma from NTUA n 1979, hs M.Sc. n Communcaton Engneerng n 1980 from UMIST n England and hs Ph.D n Sgnal Processng from the Computer Scence Dvson of NTUA. He s wth the Electrcal Engneerng Department of NTUA snce 1986 where he serves now as a Professor. Snce 1990 he s Drector of the Image, Vdeo, and Multmeda Systems Laboratory of NTUA. He has publshed more than 10 papers n the above felds, 50 of whch n nternatonal journals. He has been a member of the Techncal or Advsory Commttee or nvted speaker n 40 Internatonal Conferences. He s a revewer of 10 IEEE Transactons and of 10 other journals. Dr. A.S.Drgas Eng & psych s Senor Researcher at IIT-NCSR Demokrtos. He s Coordnator of Telecoms & founder of Net Meda Lab snce to 1999 was Operatonal manager of Greek Academc network. Coordnator of Several Internatonal & Natonal Projects, n the felds of ICTs Telecoms, e-servces e-learnng, e-psychology, e- government, e-ncluson, e-culture, e-busness etc, He has publshed more than 00 nternatonal & natonal artcles n ICTs, 7 books, 5 educatonal CD-Roms, & several patents. He has been member n several Internatonal & Natonal commttees for desgn and coordnaton Network & ICT servces & actvtes, and also n several commttees of nternatonal conferences & journals. He has also receved several dstnctons for hs scentfc work artcles, projects, patents.