Blind Steganalysis for Digital Images using Support Vector Machine Method

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1 06 Iteratoal Symposum o Electrocs ad Smart Devces (ISESD) November 9-30, 06 Bld Stegaalyss for Dgtal Images usg Support Vector Mache Method Marcelus Hery Meor School of Electrcal Egeerg ad Iformatcs Badug Isttute of Techology Badug, Idoesa herymeor@yahoo.com Rald Mur School of Electrcal Egeerg ad Iformatcs Badug Isttute of Techology Badug, Idoesa rald@formatka.org Abstract Bld stegaalyss s a method used to detect whether there s a hdde message a meda wthout havg to kow the stegaoy algorthm behd t. Dgtal mage s coverted to features usg feature extracto algorthm subtractve pxel adacecy matrx. A model s bult based o the resultg features usg mache learg method support vector mache. The support vector mache method has dfferet kerel cofgurato optos, whch are lear, polyomal, ad Gaussa. The model that has bee bult the udergoes a testg to measure the accuracy performace message detecto ad message legth estmato. From the model testg, t s obtaed that the accuracy message detecto shows good result whle the accuracy message legth estmato does ot. Hghest accuracy s obtaed wth polyomal kerel. Keywords bld stegaalyss, support vector mache, dgtal mages, feature extracto I. INTRODUCTION Stegaoy s a techque related to hdg message a partcular meda. Commo meda used are mage, vdeo, audo, ad text. The hdde message s usually the form of text, but t does ot rule out the possblty that t s aother type of meda, lke mage ad audo. Image s the most frequetly used meda for message hdg. Image cossts of pxels, each of whch cotas color bt. The commo stegaoy techque s spatal doma method,.e. hdg message a mage by replacg some of the bts the mage. Ths method s qute popular because t oly slghtly chages the orgal mage ad the hdde message has a farly large sze. Nowadays, stegaoy has grow so much that more people start to thk about how to reverse the process. The techque to do so s called stegaalyss. Stegaalyss ca be dvded to two, targeted stegaalyss ad bld stegaalyss. Targeted stegaalyss s doe by reversg the stegaoy algorthm so that t ca be see whether there s a message hdde a mage. However, targeted stegaalyss eeds formato about what algorthm used the message hdg. Bld stegaalyss s developed because ot every mage s kow for ts message hdg method. Ths method s ot always accurate, but very useful f we do ot have ay formato about the stegaoy algorthm used. Besdes detectg whether there s a hdde message, bld stegaalyss s also developed to detect mportat attrbutes such as message legth ad algorthm used to reach the goal of stegaalyss, whch s fgurg out the cotet of the hdde message. Bld stegaalyss techque ca be combed wth mache learg to obta better result. Stegaalyss requres mache learg method the form of bary classfcato to determe whether a dgtal mage cotas hdde message. The mache learg method that has form of bary classfcato s Support Vector Mache [4]. Support vector mache s also very good for classfcato wth umerous features. II. THEORY A. Stegaalyss Stegaalyss s techque to detect whether there s a hdde message a meda. The serto of formato o a partcular wll alter the characterstcs of the meda so that t ca be detected through several techques. Stegaalyss methods ca be dvded to two.. Specfc Techque Stegaalyss (Targeted Stegaalyss) Stegaalyss for certa stegaoy method such as LSB (Least Sgfcat Byte). Ths method has great accuracy f the method used stegaoy s the same as oe the stegaalyss.. Bld stegaalyss Ths stegaalyss method does ot emphasze o a specfc, but for all stegaoy methods. Ths method s used by aalyzg chages pxel bt or by statstc. Fgure.Workflow of stegaalyss process Stegaalyss process starts from a stego-mage that s suspected to have hdde message. From the stego-mage, features are extracted usg feature extracto method that has bee defed. The, the gaed features are put to a classfer /6/$ IEEE 3

2 to determe whether the mage cotas hdde message. Aother result of the classfer ca be the legth of the message or the stegaoy algorthm used to do the message hdg. Stegaalyss process s show Fgure. B. Support Vector Mache Support Vector Mache (SVM) s a mache learg method based o bary classfcato. Bary classfcato s a method that dvdes data to two classes (bary) where each data wll have class value + or -. For each data, ( x, y) wth d = N, x R, ad y {, + }, bary classfcato f( x ) results as follows. +, f ( x ) 0 y = ( ), f ( x ) < 0 x stads for dataset, whch s a collecto of real umbers a as attrbute, ad k s the umber of attrbutes the data. x a a a k = ( ) SVM method wll form a support vector based o data that s closest to the separatg hyper plae so that t wll be formed oe support vector for each class. Ths support vector wll assst classfyg determg cofdece. If data s located betwee the support vector, the the data s classfed wth lower cofdece tha oe below or above the support vector. Fgure shows the represetato of support vector llustrated by the dotted le. Fgure. Support vector represetato Below are the steps to get hyper plae ad support vector to form the model [5].. Every data has value α. Ths value represets the fluece of the data o the hyper plae ad support vector.. Calculate the value α for each data so that L D gets maxmal value wth the followg equato. LD( α) α αα yyk( xx) ( 3 ) = =, = Wth terms α y = 0 ad c α 0, where c s a = determed costat. The kerel used s lear kx (, x) = x x,polyomal k( x, ) ( ) x = x x, or Gaussa k( x, x ) = γ x x 3. After L D has maxmal value, save every data that has value α > 0. The data wll be support vector. 4. Classfy fucto s: s f( x ) = α y x x + b ( 4 ) d d = 5. To do classfcato o a data x, equato (4) s used for calculato frst. The, equato () s used to do the classfcato. To calculate the maxmal value of L D, several algorthms ca be used, for stace, modfed sequetal mmal optmzato [6]. Support vector mache s bary classfer, but t does ot rule out the possblty to buld mult-class model wth ths method. Mult-class model for support vector mache cossts of several sub-models. Some way to buld support vector mache mult-class model are oe-agast-all, oe-agast-oe, ad drected [4]. III. RELATED WORKS Stegaalyss method proposed by Swe ad Hay [7] uses color wavelet statstcs as feature extracto ad support vector mache as classfer. Ths method ca be doe both spatal doma ad trasformato doma. However, the accuracy spatal doma s lower tha oe trasformato doma for the feature extracto method s very close to trasformato doma. Stegaalyss method proposed by Jag [8] uses the combato of several methods. For feature extracto, flter s used the form of shftg ad also frst order statstcs. For classfer, esemble classfer s used wth addto of AdaBoost ad baggg. Ths stegaalyss method s doe spatal doma ad s made to cope wth the low accuracy sertg text a small sze. But the feature extracto method used has a qute large dmeso. Stegaalyss method proposed by Tomas [9] uses subtractve pxel adacecy matrx as feature extracto method ad support vector mache as classfer. Ths stegaalyss method s doe spatal doma. Ths method emphaszes o feature extracto ad detecto of suspcous pxel. Support vector mache method used s oly based o Gaussa kerel. IV. PROPOSED METHOD To make a model to detect hdde message ad estmate message legth, frst, we geerate dataset ad testset. Dataset s used for model trag ad testset s used for model testg. Dgtal mages for dataset ad testset are derved from teret 33

3 wth varous sze ad type. Work flow for proposed method s show Fgure 3. Fgure 3. Workflow for proposed method Dataset ad testset are cover-mage ad stego-mage from the dgtal mages collected before. Dgtal mages dataset ad testset are coverted to same sze ad type, whch s 800x600 pxel btmap type. For dataset, stego-mage s obtaed by sertg message sze of 0.05, 0.5, 0.5, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, ad 0.95 bpp. For testset, stego-mage s obtaed by sertg message radom sze that rages from 0.0 to 0.99 bpp. The serto of the message to dgtal mages uses stegaoy tools, Steghde [0], Four Pxel [], ad Ne Pxel []. Feature extracto phase s a process to obta mportat features from dgtal mage dataset ad testset. Feature extracto algorthm used s subtractve pxel adacecy matrx proposed by Tomas [9]. The algorthm s as follows.. Calculate dfferece matrx D mages wth wdth m pxel ad heght pxel for rght drecto. The equato for dfferece matrx s D = I I ( 5 ),,, + subect to {,, m} ad {,, }. I, s pxel value at pxel (, ). For color mages, pxel value s average of red, gree, ad blue value. Example for calculatg dfferece matrx mages wth 4x4 pxel sze ca be see Fgure 4. Fgure 4. Dfferece matrx for 4x4 pxel sze mage. Calculate value for Markov cha wth equato (6) Mu, v Pr ( D, + u D, subect to uv, { 4,,4}. If ( D, Mu, v Pr ( D, + u D, 0 = = = ( 6 ) Pr = = 0 the = = = =. There wll be 8 values of Markov Cha. Example for calculatg Markov Cha Fg. 4 ( + ) ( + ) M0,0 = Pr D, = 0 D, = 0 = M, = Pr D, = D, = = Repeat step oe ad two for the other drecto 4. Smplfy the features wth equato (7) ad (8) F,,8 = M + M + M + M 4 ( 7 ) F8,6 = M + M + M + M 4 ( 8 ) so, there wll be 6 features for each mage. Trag model wth support vector mache requres dataset features as put. From the collecto of features, two models wll be bult. The frst model s used to determe whether the dgtal mage cotas hdde message, whle the other model s used to estmate the hdde message legth. The frst model has lear, polyomal, ad RBF as kerel cofgurato optos, whle the secod model has addtoal cofgurato whch s mult-class optos, cossts of oe-agast-all, oe-agast-oe, ad drected. Model learg wth support vector mache method uses modfed sequetal mmal optmzato algorthm proposed by Cao [6]. Testg phase requres the model that has bee bult before as put. The model s tested wth dataset ad testset to measure ts performace. Each testg results a accuracy value. V. EXPERIMENT Model trag s doe maxmal terato 3000, costat c 00, two classes for message detecto, whch are yes ad o, ad fve classes for message legth estmato, whch are very low (<0. bpp), low (0. bpp 0.4 bpp), medum (0.4 bpp 0.6 bpp), hgh (0.6 bpp 0.8 bpp), ad very hgh ( >0.8 bpp). Model testg process s dvded to four steps, testg for message detecto grayscale mages, testg for message legth estmato grayscale mages, testg for message detecto color mages, ad testg for message legth estmato color mages. The accuracy of message detecto ad message legth estmato s calculated wth followg equato. umber of correct classfed mages accuracy = ( 9 ) umber of mages testset Table. Testg result for message detecto grayscale mages Kerel wth Dataset wth Testset lear 67.5% 63% polyomal 75.5% 73% Gaussa 69.5% 59% 34

4 Table Testg result for message detecto color mages Kerel wth Dataset wth Testset lear 54.75% 57% polyomal 64.5% 6% Gaussa 5.5% 50% Table 3 Testg result for message legth estmato grayscale mages Table 4 Testg result for message legth estmato color mages Kerel Mult-class wth Dataset wth Testset lear oe-agastall 5.% 4% lear oe-agastoe 8.5% 4.8% lear drected 9.5% 4.8% polyomal oe-agastall 6.45% 6.8% polyomal oe-agastoe 39.4% 33.6% polyomal drected 38.5% 30% Gaussa oe-agastall 7.95% 6% Gaussa oe-agastoe 30.95% 5.% Gaussa drected 30.55% 4.4% Kerel Mult-class wth Dataset wth Testset lear oe-agastall 3.5% % lear oe-agastoe.35% 3.6% lear drected.3%.6% polyomal oe-agastall 0% 0% polyomal oe-agastoe 6.35% 3.6% polyomal drected 6.05% % Gaussa oe-agastall 5.5% 4% Kerel Mult-class wth Dataset wth Testset Gaussa oe-agastoe 0.9% 0.8% Gaussa drected.05% 0.8% The testg result for message detecto grayscale mages shows that cofgurato wth polyomal kerel has the hghest accuracy wth both dataset ad testset. Polyomal kerel cofgurato results better because polyomal s more flexble tha lear ad more rgd tha Gaussa. Plae created by polyomal kerel s ot a flat plae, but wavy as t adusts to the data, whle plae created by lear kerel s more rgd that t does ot really match wth the data. Plae created by Gaussa kerel s too flexble that t turs the model to overft. Overft s the state whe the resultg model s too depedet o dataset that t does ot create model that s more commo. The result for message detecto color dgtal mage testg also shows that cofgurato wth polyomal kerel has the hghest accuracy. A commo model s eeded so that ose data does ot affect the model. The result for message legth estmato grayscale dgtal mage testg shows that cofgurato wth polyomal kerel ad mult-class oe-agast-oe has the hghest accuracy wth both dataset ad testset. The message legth estmato color dgtal mage testg also shows the same result. Testg result shows that polyomal kerel stll better tha two other kerels. The comparso of accuracy result from testg grayscale mages ad color mages ca be see Fgure 5. The comparso shows that accuracy of grayscale mages testg s geerally hgher tha the accuracy of color mages testg. Ths happes due to the dfferece feature extracto for grayscale mages ad color mages. I grayscale mages, the dfferece betwee each pxel ca be easly calculated because t oly cotas oe value rage 0-55, whle color mages, each pxel cotas three values each for red, gree, ad blue. Chages the value of the pxel to aother pxel ear t become more dffcult to detect because values each pxel cotas are coverted to grayscale value. For example, red, gree, ad blue values for oe pxel are, 5, ad 0, whle for the other pxel the values are 3,, ad. The dfferece of pxel values s beg calculated ad resultg a zero value, t happes because both pxels have same grayscale value. for message legth estmato s ot good eough because the feature extracto method used does ot match. Subtractve pxel adacecy matrx oly dcates probablty value that chages. As a result, f there s a hdde message, the hdde message ca be kow usg support vector mache method. Subtractve pxel adacecy matrx does ot show how much the pxel chages so t s ot sutable for hdde message legth estmato. 35

5 (%) Comparso of accuracy from testg result for grayscale mages ad color mages Fgure 5 Comparso of accuracy from testg result for grayscale mages ad color mages. VI. Test cofgurato grayscale CONCLUSION color Bld stegaalyss wth support vector mache method ca be mplemeted to detect hdde message ad estmate hdde message legth dgtal mage. The model that s bult wth the support vector mache method has a qute good result detectg the hdde message wth a accuracy of 73% for grayscale mage ad 6% for color mage. The hdde message legth estmato dgtal mage has poor result. The hghest accuracy acheved s equal to 33.6% for grayscale mage ad 3.6% for color dgtal mage. Cofgurato wth polyomal kerel s better tha the other two, lear ad Gaussa. ACKNOWLEDGMENT Author would lke to thak Dr. Ir. Rald Mur, M.T. as the supervsor for the gudace ad valuable kowledge durg ths research. REFERENCES [] Hussa, M., & Hussa, M. (03). A survey of mage stegaoy techques. [] Chadramoul, R., Kharraz, M., & Memo, N. (003, October). Image stegaoy ad stegaalyss: Cocepts ad practce. I Iteratoal Workshop o Dgtal Watermarkg (pp ). Sprger Berl Hedelberg. [3] Frdrch, J., Gola, M., Hogea, D., & Soukal, D. (003). Quattatve stegaalyss of dgtal mages: estmatg the secret message legth. Multmeda systems, 9(3), [4] Hsu, C. W., & L, C. J. (00). A comparso of methods for multclass support vector maches. IEEE trasactos o Neural Networks, 3(), [5] Schölkopf, B., & Smola, A. J. (00). Learg wth kerels: support vector maches, regularzato, optmzato, ad beyod. MIT press. [6] Cao, L. J., Keerth, S. S., Og, C. J., Zhag, J. Q., Peryathamby, U., Fu, X. J., & Lee, H. P. (006). Parallel sequetal mmal optmzato for the trag of support vector maches. IEEE Trasactos o Neural Networks, 7(4), [7] Lyu, S., & Fard, H. (004, Jue). Stegaalyss usg color wavelet statstcs ad oe-class support vector maches. I Electroc Imagg 004 (pp ). Iteratoal Socety for Optcs ad Photocs. [8] Yu, J., Zhag, X., & L, F. (05). Spatal stegaalyss usg redstrbuted resduals ad dverse esemble classfer. Multmeda Tools ad Applcatos, -3. [9] Pevy, T., Bas, P., & Frdrch, J. (00). Stegaalyss by subtractve pxel adacecy matrx. IEEE Trasactos o Iformato Forescs ad Securty, 5(), 5-4. [0] [] Lao, X., We, Q. Y., & Zhag, J. (0). A stegaoc method for dgtal mages wth four-pxel dfferecg ad modfed LSB substtuto. Joural of Vsual Commucato ad Image Represetato, (), -8. [] Swa, G. (04). Dgtal mage stegaoy usg e-pxel dfferecg ad modfed LSB substtuto. Ida Joural of Scece ad Techology, 7(9),