Keywords: watermarking, Security, Encode and Decode,Fragile watermarking,psnr ratio.

Transcription

1 Volume 5, Issue 9, September 2015 ISSN: X Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng Researh Paper Avalable onlne at: Improved Reversble Watermarng Usng Hash Funton K. Srlashm, K. Srnvas Department of CSE, Gudlavalleru Engneerng College, Inda Abstrat: Proteton of dgtal multmeda ontents, as well as seurty and prvay have beome a major hallenge due to the reent advanes n the felds of networng, and dgtal multmeda, as well as the readly avalablty of opyng and manpulatve deves and software programs to the publ. One possble explanaton s that the tradtonal wavelet transform oeffents are more senstve to flterng and nose than orgnally expeted. Hene, the embedded mar ould not survve these modfatons. One way to overome ths problem s to use a hash robust watermar embeddng algorthm. Expermental results show that proposed model outperformed well aganst tradtonal approahes. Keywords: watermarng, Seurty, Enode and Deode,Fragle watermarng,psnr rato. I. INTRODUCTION Dgtal Watermarng (also nown as Multmeda data Embeddng, Steganography) protets the opyrght of dgtal mages. Reversble watermarng or Lossless Data embeddng has to meet the requrements are Robustness, mpereptblty, embeddng and retrevng, hgh embeddng apaty.usually Watermarng proess ntrodues rreversble degradaton of the orgnal medum.for lassal watermarng, the mages wth hgh ontent of textures provde more apaty for data hdng than the one wthout textures. Ths s due to the fat that the human vsual system s less senstve to degradaton n textured areas and thus one an embed more n textured than n unform areas (see [1], et.). For reversble watermarng, ths s not true: one an embed more data n unform than n textured areas. Ths an be easly explaned by the fat that the reversble watermarng shemes are based on pxel orrelaton, and the orrelaton n textured areas s lower than n unform areas. We remnd that reversble watermarng not only demands mpereptble embeddng of data, but also the reovery of the orgnal host wthout any errors. The algorthms provdng the hghest embeddng bt-rates are the so alled dfferene expanson reversble watermarng [2, 3], et. Dfferene expanson reates room for data embeddng nto the least sgnfant bt (LSB) of a ertan pxel based dfferene, usually n the predton error. More presely, pxels are modfed suh that, the predton error at deteton be two tmes larger. The multplaton by two sets to zero the LSB and leaves room for a bt of data. Even a very slght hange n pxel values may not be aeptable for mltary, medal data and also multmeda arhvng of valuable orgnal wors. By embeddng ts message authentaton ode, Reversble data embeddng provde True self authentaton sheme. If nose le sgnal an be added to mages, audo, vdeo Steganography [2] protet t from alteraton and authentate ts ontent. [3] Presented the RS sheme to embed watermar bts nto the status group of pxels.fredrh also extended the tehnque to GIF and PNG mages..[4] enhaned frdrh s approah and develop a low dstorton Reversble watermar that s apable of embeddng 0.7 bts per pxel. Generalzaton of LSB [5] based on the sheme by applyng data ompresson.from the lterature revew we have seen that most of the RW sheme belongs to fragle watermarng tehnque.fragle watermarng s a type of watermarng tehnque whh embeds seret nformaton alled watermar nto an mage that s not readable anymore f the ontent gets altered by any nd of ntentonal on unntentonal attas. And the term sem ndates f t an sustan some attas, moreover, f any watermar an sustan wth all nds of attas s alled robust watermarng tehnque. Fragle watermarng s the subjet of many applatons le ontent authentaton. Reversble Watermarng s a speal ase of dgtal watermarng wth an ntrgung feature that when the watermared ontent has been authentated, one an remove the watermar to retreve the exat same orgnal nformaton. Suh reversblty s hghly desrable n ase of hgh seured data ommunaton, espeally for medal and mltary applatons where even a small amount of dstorton s not permssble. II. LITERATURE SURVEY IMAGE PARTITIONING AND DISTORTION CONTROL It s seen that a natural mages possess dfferent haratersts le smooth, edge, texture regon, et. and dfferent regons are n a dfferent way senstve to embeddng dstorton. Ths needs adaptve watermar power ontrol so that the overall struture of the watermared mage s less affeted. In an automat dentfaton of mage regons, t s dffult to fnd threshold gray values due to mprese and unertantes n gray values. Here, we partton an mage nto three omponents usng the omputed gradent magntudes [5]; gves a smple mage partton method that we used to obtan dfferent mage regons. 2015, IJARCSSE All Rghts Reserved Page 723

2 Srlashm et al., Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng 5(9), Chang D.Yoo et al. (2007), explans hgh apaty and low dstorton reversble mage watermarng usng nteger to nteger wavelet transform (IWT) and ths ondton to avod overflow/underflow n the spatal doman are derved for an arbtrary wavelet and blo sze. They desrbe the most popular method of watermarng whh nludes spread spetrum suh as addtve and multplatve spread spetrum. [5] Proposed lossless watermarng based on a rular nterpretaton of Objetve transformatons. Tan (2003), gave one of the frst few development of reversble watermarng. Yang et al. (2004) proposed a reversble watermarng tehnque based on the Dsrete Cosne Transform. [6] Adopted the hstogram shftng tehnque to embed the loaton map that the DE sheme requred. They then proposed a PEE method to embed watermars. They present multbt, multplatve, spread spetrum watermarng usng the dsrete multwavelet. [7] proposed a reversble watermarng method n medal and defense magery wth the ombnaton of IWT and Genet algorthm. They desrbe another popular lass of reversble watermarng algorthm s based on the hybrd IWT. IWT based adaptve data hdng sheme s to protet medal mages. They appled the ompoundng tehnque to reversbly embed a large amount of data nto an audo proposed a reversble watermarng sheme based on Integer DCT.[9] develops a blnd mage watermarng based on multband wavelet transform and the empral mode deomposton. The sheme s robust aganst JPEG ompresson, Gaussan nose, salt and pepper nose. Hstogram modfaton s another mportant tehnque n RWM.[9] ompares wavelet and multwavelet doman watermarng under a varety of attas. Furthermore, they desrbe both wavelet style and mult wavelet style watermarng under multwavelet doman watermarng. Multwavelets offer better vsual qualty than salar wavelets. Watermarng as t s used today may refer to all four ategores mentoned above. To address the ntended applatons properly, n ths thess watermarng refers to the overt watermarng and steganography ategores. Furthermore, ths thess fouses on the stll mages as a part of the multmeda ontents; all the revews, dsussons, and the proposed reversble watermarng sheme and bometr watermar framewor prnpally target dgtal stll mages. Although, the suggested algorthms an be modfed and adjusted to extend to the vdeo ontents. These shemes show a ertan degree of robustness when the watermared mage undergoes spef alteratons or proesses. In the ase of sem-fragle methods, the tolerable proess s usually onfned to a slght ompresson proess or other mld ntentonal or unntentonal hanges. On the other hand, robust shemes often present good tolerane aganst spef ntentonal attas or unntentonal sever modfatons, dependng on the purpose they are desgned for. Hene, a watermarng sheme s alled sem-fragle or robust f the extrated watermar from the modfed/proessed mared mage stays asertanable and vald. 1) Least Sgnfant Bt Codng (LSB) LSB odng s one of the earlest methods. It an beappled to any form of watermarng. In ths method thelsb of the arrer sgnal s substtuted wth the watermar. The bts are embedded n a sequene whh ats as the ey.in order to retreve t ba ths sequene should be nown.the watermar enoder frst selets a subset of pxel valueson whh the watermar has to be embedded. It thenembeds the nformaton on the LSBs of the pxels from thssubset. LSB odng s a very smple tehnque but therobustness of the watermar wll be too low. Wth LSBodng almost always the watermar annot be retrevedwthout a nose omponent [9]. 2) Predtve Codng Shemes Predtve odng sheme was planned by Matsu and Tanaa n [2] for gray sale mages. In ths method theorrelaton between neghborng pxels are broen. A set ofpxels where the watermar has to be embedded s hosenand alternate pxels are replaed by the dfferene betweenthe adjaent pxels. Ths an be further enhaned by addnga onstant to all the dfferenes. A pher ey s reatedwhh enables the retreval of the embedded watermar atthe reever. Ths s muh more robust as ompared to LSB odng. 3) Correlaton-Based Tehnques In ths method a pseudo random nose (PN) wth apattern W(x, y) s added to an mage. At the deoder theorrelaton between the random nose and the mage sfound out and f the value exeeds a ertan threshold value the watermar s deteted else t s not. 4) Pathwor Tehnques In pathwor watermarng, the mage s dvded ntotwo subsets. One feature or an operaton s hosen and t sappled to these two subsets n the opposte dreton. For nstane, f one subset s nreased by a fator, the other subset wll be dereased by the same amount. If a[] s the value of the sample at I n subset A whh s nreased and b[] s the value of the sample n the subset B whose values dereased, then the dfferene between the two subsetswould ntutvely result n -->Σ(a[]-b[]) =2N for watermared mages 1<=N<= = 0 otherwse III. PROPOSED WORK In the past deade, applaton of dgtal multmeda ontents has grown rapdly beause of ther advantages over analog ontents. Ease of transferrng and broadastng over networs, hgher qualty and durablty, onlne/offlne easy edtng, opyng, and smplty of arhvng or are just storng a few advantages of dgtal multmeda over analog ontents. Ironally, all the above advantageous propertes have rased the man onerns n opyrght management and prvay proteton of suh ontents. 2015, IJARCSSE All Rghts Reserved Page 724

3 Srlashm et al., Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng 5(9), Enrypton methods suh as onventonal onneton-based seurty systems annot arry out the requred proper proteton level as t s mpossble to montor how a legtmate user handles the ontent after derypton, whh maes t possble for haers and adversares to llegally redstrbute or manpulate the ontent. The enoder reates the symbols from the nput message and transmts them aross a nosy hannel, then at the reever sde the enoder reonstruts the orgnal message from the reeved nosy transmtted symbols. To ensure the seurty of the model and the transmsson seret eys an be employed at the enoder and deoder. The same gener model an be adopted to llustrate a dgtal watermarng system. In a dgtal watermarng model the to-be-embedded watermar an be onsdered as the nput message, the over meda plays the same role as the nosy hannel and the detetor has the same funton as the deoder. Fg. 1 s a blo dagram of a watermarng system usng ontent-dependent watermars. The emphass of ths hapter s to desgn a robust hash funton whh an be used to generate a desrpton of the orgnal sgnal. Ths desrpton s then embedded nto the over sgnal. In authentaton, the watermar s ompared to the orgnal sgnal. Conventonal ryptograph hash funtons suh as MD5 and SHA-1 are very senstve to hanges n the nput sgnal. Generally, a sngle-bt hange wll produe a ompletely dfferent hash. Sne the over mage wll undoubtedly be dfferent after watermar embeddng, an mage hash funton should be robust to pereptually nsgnfant modfatons. Fg 1: Conventonal Model Proposed Model Pre-proessng methods use a small neghborhood of a pxel n an nput mage to get a new brghtness value n the output mage. The am of pre-proessng s an mprovement of the mage data that suppresses unwanted dstortons or enhanes some mage features mportant for further proessng.in ths system we are onvertng dfferent mage formats nto JPEG-LS mage as JPEG-LS s a lossless ompresson standard for mages and was developed wth the am of provdng a low-omplexty lossless ompresson standard that ould offer better ompresson effeny than lossless JPEG. Lossless mage ompresson attempts to retan absolutely all the nformaton present n the orgnal mage. Fg 2: Proposed Model Proposed Algorthm Step 1: Apply the Gaussan Low pass flter: The low pass flterng model an be represented by usng Gaussan Funton G(p,q) and an mage I(p,q). Gf ( p, q, ) G(p,q)*I(p,q). 2 I (p,q) Where s the standard devaton of the dstrbuton. Step 2: Normalze the sze of the I(p,q) q (, ) ( p )/2 X p q G(p,q) (1/ 2 )*e 2015, IJARCSSE All Rghts Reserved Page I (p,q)

4 Srlashm et al., Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng 5(9), N= ( I( p, q) max( p, q)) / (max(p,q) mn(p,q)) L2 Norm an be proessed usng the followng vetor representaton. ve ve ve 2 2 /, 1,2,... ve s a vetor onsstng of HoG desrptors of ell. For =1, vetor ells UL,UC and L. For =2, vetor ve ontans ve and the desrptors of ells UR,UC and R. For =3, vetor ve ontans ve and the desrptors of ells LR,LC and R. For =4, vetor ve ontans ve and the desrptors of ells LL,LC and L. Step 3: Compute the loal hstogram of the Image I. Hst(I)=LoalHst( ve ); ve ontans ve and the desrptors of Step 4: Watermar feature extraton usng ompressed odng. Watermar ompressed feature s omputed by usng N1 N2 CF(, j) (1/ N N ) d ( m, n) F max( CF(, j)) Step 5: In order to onstrut the hash eah ompressed feature followng equaton. hash = 0, f F var( F) =1, f F var( F) 1 2 (, j) m 1 n 1 { hash, hash, hash, hash... hash }. F s transformed nto the bt strng 3 hash usng the Step 6: Feature hash vetor h= n Watermar reovery s usually more robust f the orgnal, unwatermared data are avalable. Further, avalablty of the orgnal data set n the reovery proess allows the deteton and nverson of dstortons whh hange the data geometry. Ths helps, for example, f a watermared mage has been rotated by an attaer. However, aess to the orgnal data s not possble n all ases, for example, n applatons suh as data montorng or trang. IV. EXPERIMENTAL RESULTS Fg 3: Loadng soure mage Fg 4: Embedded Watermar 2015, IJARCSSE All Rghts Reserved Page 726

5 Srlashm et al., Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng 5(9), Fg 5: Computng Hash Value Fg 6: Enter hash value for Extraton Fg 7: Entered hash Value Fg 8: Extrated Logo Fg 9: Extrated soure mage Table 1: Computatonal performane Model Enodng Tme DeodngTme RunTme DCT model reversble model Proposed model , IJARCSSE All Rghts Reserved Page 727

6 Srlashm et al., Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng 5(9), Fg 10: Performane graph Table 2: Loal predton table Model Btrate LoalPredton DCT model reversble model Proposed model Fg 11: Comparson wth other other models Table 3: PSNR rato wth Loal predton Model PSNR rato LoalPredton DCT model reversble model Proposed model Fg 12: Graph omparson wth other models V. CONCLUSION The hash based watermar embeddng tehnque presented n ths paper s robust aganst hgh qualty mages and other mage proessng operatons. One possble explanaton s that the tradtonal wavelet transform oeffents are more senstve to flterng and nose than orgnally expeted. Hene, the embedded mar ould not survve these modfatons. One way to overome ths problem s to use a hash robust watermar embeddng algorthm. Expermental results show that proposed model outperformed well aganst tradtonal approahes. 2015, IJARCSSE All Rghts Reserved Page 728

7 Srlashm et al., Internatonal Journal of Advaned Researh n Computer Sene and Software Engneerng 5(9), REFERENCES [1] Robust Watermarng of Compressed and Enrypted JPEG2000 Images A.V.Subramanyam,Sabu Emmanuel,Member,IEEE, and Mohan S.Kananhall,Senor Member,IEEE. [2] CHAKRA: A New Approah for Symmetr Key Enrypton,P.Ramesh Kumar,S.S.Dhenaaran, IEEE Internatonal Conferenes on Aousts, Speeh and sgnal proessng, Vol.4, 1996, pp [3] A.Subramanyam, S.Emmanuel, and M.Kananhall Compressed enrypted doman JPEG2000 Image watermarng, n Pro. IEEE Int. Conf. Multmeda and Expo, 2010, pp [4] QuntngYang A Novel Sem-fragle Authentaton Watermarng Sheme of Color JPEG Image n Compressed Doman ollege of Informaton Sene,Nana Unversty Tanjn, Chna.2010 Internatonal Conferene on Multmeda Informaton Networng and Seurty [5] R. Shyndel, A. Trel, and C. Osborne, A dgtal watermar, n IEEE Pro. Int. Conf. Image Proessng, 1994, vol. 2, pp [6] A robust multple watermarng sheme n the Dsrete Wavelet Transform doman by Penng Taoa and Ahmet M. Esoglub The Graduate Center, The Cty Unversty of New Yor, 365 Ffth Avenue, New Yor. [7] A robust multple watermarng sheme n the Dsrete Wavelet Transform doman by Penng Taoa and Ahmet M. Esoglu Department of Computer and Informaton Sene, Broolyn College, The Cty Unversty of New Yor, 2900 Bedford Avenue, Broolyn, NY [8] W. Bender, D. Gruhl, N. Mormoto, and A. Lu, Tehnques for data hdng, IBM Systems Journal, vol. 35, no. 3-4, pp , [9] R.B.Wolfgang and E.j.Delp, A watermar for dgtal mages n IEEE pro.int.onf.image Proessng,1996,vol.3,pp [9] M. D. Swanson, B. Zhu, and A. H. Tewf, Transparent robust mage watermarng, n IEEE Pro. Int. Conf. Image Proessng, 1996, vol. 3, pp [10] J. Cox, J. Klan, T. Leghton, and T. Shamoon, Seure spread watermarng for multmeda, IEEE Trans. Image Proessng, vol. 6, no. 12, pp , De [11] M. Barn, F. Bartoln,a and A. Pva, Improved wavelet-based watermarng through pxel-wse masng, IEEE Trans. Image Proessng, vol. 10, no. 5, pp , [12] X. Xa, C. G. Bonelet, and G. R. Are, A multresoluton watermar for dgtal mages, n IEEE Pro. Int. Conf. Image Proessng, USA, 1997, pp [13] J. R. Km, and Y. S. Moon, A robust wavelet-based dgtal watermarng usng level-adaptve thresholdng, n IEEE Pro. Int. Conf. Image Proessng, Japan, 1999, pp [14] T. Hsu, and J. L. Wu, Hdden dgtal watermars n mages, IEEE Trans. Image Proessng, vol. 8, no. 1, pp , Jan [15] Dugad, K. Rataonda, and N. Ahuja, A new wavelet-based sheme for watermarng mages, n IEEE Pro. Int. Conf. Image Proessng,USA, 1998, pp , IJARCSSE All Rghts Reserved Page 729