Reliable information hiding based on support vector machine.docx
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Reliable information hiding based on support vector machine.docx
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Reliableinformationhidingbasedonsupportvectormachine
ReliableInformationHidingBasedonSupportVectorMachine
Yong-GangFu1,3,Rui-MinShen1,Li-PingShen1andXu-ShengLei2
1Dept.ofComputerScienceandEngineering,ShanghaiJiaotongUniv.,Shanghai,China,200030
2Dept.ofAutomation,ShanghaiJiaotongUniv.,Shanghai,China,200030
3Dept.ofSoftware,XiamenUniv.,Xiamen,Chian,361000
E-mails:
{fyg,rmshen,lpshen,xushenglei}@
Abstract:
Inthispaper,areliableinformationhidingschemebasedonsupportvectormachineanderrorcorrectingcodesisproposed.Toextractthehiddeninformationbitsfromapossiblytamperedwatermarkedimagewithalowererrorprobability,informationhidingismodeledasadigitalcommunicationproblem,andboththegoodgeneralizationabilityofsupportvectormachineandtheerrorcorrectioncodeBCHareapplied.Duetothegoodlearningabilityofsupportvectormachine,itcanlearntherelationshipbetweenthehiddeninformationandcorrespondingwatermarkedimage;whenthewatermarkedimageisattackedbysomeintentionalorunintentionalattacks,thetrainedsupportvectormachinecanrecovertherighthiddeninformationbits.Thereliabilityoftheproposedschemehasbeentestedunderdifferentattacks.Theexperimentalresultsshowthattheembeddedinformationbitsareperceptuallytransparentandcansuccessfullyresistcommonimageprocessing,jitterattack,andgeometricaldistortions.Whenthehostimageisheavilydistorted,thehiddeninformationcanalsobeextractedrecognizably,whilemostofexistingmethodsaredefeated.Weexpectthisapproachprovideanalternativewayforreliableinformationhidingbyapplyingmachinelearningtechnologies.
Keywords:
Informationhiding;Supportvectormachine;Digitalwatermarking;BCHcoding
1.Introduction
DigitalpropertiesarereadilyreproducedandredistributedovertheInternetandothermedias.Howevertheseattractivepropertiesleadtoproblemsenforcingcopyrightprotectionissues.Asaresult,thecontributoranddistributorofthedigitalpropertiesarehesitanttoprovidetheaccesstotheirintellectualproperties.Itisrealizedthatconventionalcryptographicmeansarenotsufficientsincethedataiswithoutanyprotectionassoonasitisused,e.g.,decryptedanddisplayedinthecaseofimageorvideodata.Apotentialapproachtosolvethisproblemisinformationhidingordigitalwatermarking(Swansonetal.,1998).Informationhidingistheimperceptibleembeddingofinformationbits(signature)intomultimediadata,wheretheinformationremainsdetectableaslongthequalityofthecontentitselfisnotrendereduseless.Asabranchofinformationhiding,itiscommonlyassumedthatdigitalwatermarkingisonlyoneofseveralmeasuresthathavetobecombinedtobuildagoodcopyprotectionmechanism(FuronandDuhamel,2000).Asignificantmeritofdigitalwatermarkingovertraditionalprotectionmethodsistoprovideaseamlessinterface,sothatusersarestillabletoutilizetheprotectedmultimediatransparently.
Aninformationhidingschemeshouldatleastmeetthefollowingrequirements:
(1)Perceptualinvisible(ortransparent).
(2)Difficulttoremovewithoutseriouslyaffectingtheimagequality.(3)Robustresistancetoimageprocessing,andattacks.
Developinganalgorithmcapableofproducingsignaturethatfulfillsalltheserequirementsisnotaneasytask.Ononehand,theinformationhidingprocessshouldnotintroduceanyperceptibleartifactsintothehostimage.Ontheotherhand,forhighrobustnessitisdesirablethatthemarkamplitudeisashighaspossible.Therefore,thedesignationofinformationhidingmethodalwaysinvolvesatradeoffbetweenimperceptibility(ortransparency)androbustness.Avarietyofwatermarkingorinformationhidingschemeshavebeenreportedrecentlyintheliterature,andsomenicereviewscanbefoundin(Fabienetal.,1999).However,theresearchoncopyrightprotectionofimagesisstillinitsearlystageandnoneoftheexistingmethodsistotallyeffectiveagainstmaliciousattacks.
Thereareavarietyofschemesforhidinginformationintotheoriginalimage.Typicalschemesfortheinformationhidinginimagescanbebroadlyclassifiedintotwocategories:
(i)spatialdomainmethodswhichembedthedatabydirectlymodifyingthepixelvaluesoftheoriginalimage(NikolaidisandPitas,1998);(ii)transformdomainmethodswhichembedthedatabymodulatingthecoefficientsofproperlychosentransformdomainsuchasDCT(Coxetal.,1997;Barnietal.,2000),DFT(Barnietal.,2000),andDWT(Xiaetal.,1998).Manyofthespatialdomaintechniquesprovidesimpleandeffectiveschemesforembeddinganinvisiblewatermarkintoanimagebutarenotrobusttocommonattacks.Informationhidingtechniquescanbealternativelysplitintotwodistinctcategoriesdependingonwhethertheoriginalimageisnecessaryforthewatermarkextractionornot.Althoughtheexistenceoftheoriginalimagefacilitateswatermarkextraction(Coxetal.,1997;Swansonetal.1996;PodilchukandZeng,1998)togreatextent,sucharequirementraisestwoproblems:
(i)owneroftheoriginalimageiscompelledinsecurelytosharehisworkswithanyonewhowantstochecktheexistenceofthesignature(Barnietal.,1998),(ii)ontheotherhand,thesearchingwithinthedatabasefortheoriginalimagethatcorrespondstoagivenwatermarkedimagewouldbeverytimeconsuming.Thus,methodscapableofrevealingtheinformationbitspresencewithoutcomparingthewatermarkedandoriginalimageswouldbepreferable.
Inordertodesignrobustinformationhidingscheme,Coxetal.consideredwatermarkingasaproblemofcommunicationwithsideinformation(Coxetal.,1999).Also,somewatermarkingalgorithminliteratureappliederrorcorrectingcoding(ECC)toimprovethebiterrorrate(BER)performance,suchasBose-Chaudhuri-Hocquenghen(BCH)coding(Huangetal.1998;HuangandYun,2002),Reed-Solomon(R-S)code(WuandHsieh,2000),andTurbocode(PereiraandPun,2000).Recently,effortsaremadetouseartificialintelligencetechniqueforwatermarkembeddingandextraction.Neuralnetworksareintroducedintowatermarkingin(Yuetal.,2001),whichmakesthewatermarkextractionmorerobustagainstcommonattacks.GeneticalgorithmisproposedforselectionofthebestembeddingpositionsinblockbasedDCTdomainwatermarking(Shiehetal.,2004).Wehavefirstlyintroducedthesupportvectormachineforthewatermarkembeddingandextractionin(Fuetal.,2004),inwhichthewatermarkisembeddedintothehostbyapplyingthegoodlearningabilityofsupportvectorregressionmachine,andthewatermarkextractionisfinishedbytheaidsofthewelltrainedsupportvectormachine.Wecanexpectthatthecombinationofinformationhidingandmachinelearningtechniquesmightbeagoodsolutionforreliableinformationhiding.
Fromtheobservationsabove,inthispaperweproposeanovelblindreliableinformationhidingandrecoveringschemewhichmakesuseofsupportvectormachineandBCHcoding.Thisworkcanbeconsideredasanextensionofsomeexistingresearch(Kutteretal.,1998;Yuetal.,2001;Fuetal.,2004).In(Kutteretal.,1998),Kutterproposedaspatialdomainwatermarkingschemeforcolorimage.ThenYu(Yuetal.,2001)improvedKutter’smethodbyapplyingneuralnetworks.Duetothesupportvectormachine’sgoodlearningabilityintrainingprocess,itcanmemorizetherelationshipbetweentheembeddedinformationandcorrespondingwatermarkedimage.ApplyingSVM’sgoodgeneralizationabilitiesanderrorcorrectingabilityofBCHcoding,hiddeninformationextractioncanbefinishedwell.Experimentalresultsshowgoodrobustnessoftheproposedschemeagainstcommonimageprocessingandattacks.Thisresearchismuchdifferentfrommyearlywork.Inthisresearch,thesupportvectormachineisonlytrainedandappliedintheinformationextractionprocedure,whereas,in(Fuetal.,2004),thesupportvectorregressionmachineisappliedbothinthewatermarkembeddingandextractionprocess.
Thepaperisorganizedasfollows:
insection2,basicconceptionsforsupportvectormachineareintroduced.Theembeddingandextractionalgorithmsofourmethodaredescribedinsection3.
Insection4,someexperimentalresultsareexhibited.Theconclusionisstatedinsection5.
2.AnoverviewofSupportVectorMachine
SupportVectorMachine(SVM)isauniversalclassificationalgorithmdevelopedbyVapnikandhiscolleagues(Vapnik,1995;Vapnik,1998).Inrecentyears,therehavebeenalotofinterestsinstudyingtheapplicationsofSVMonfunctionapproximation,patternrecognitionproblemsandsoon(Campbell,2002;Christopher,1998).
Givenatrainingdatasetofmsamples
where
istheithinputpatternand
istheithoutputpattern.
Thesupportvectormachinemethodsupposeswehavesomehyper-planesthatseparatethepositivesamplesfromnegativeones.Thepoint
whichliesonthehyper-planesatisfies
where
isnormaltothehyper-plane,
istheperpendiculardistancefromthehyper-planetotheorigin,and
istheEuclideannormof
.Forthelinearlyseparablecase,thesupportvectoralgorithmsimplylooksfortheseparatinghyper-planewiththelargestmargin.Thiscanbeformulatedasfollowing:
supposethatallthetrainingdatasatisfythefollowingconstraints:
(1)
Thiscanbecombinedintoonesetofinequalities:
(2)
Nowconsiderthepointsforwhichtheequalityin
(1)holds.Thesepointslieonthehyper-plane
withnormal
andperpendiculardistancefromtheorigin
.Similarly,thepointsforwhichtheequalityholdsin
(1)lieonthehyper-plane
withnormal
andperpendiculardistancefromtheorigin
.Hencethe
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