Research of Video Steganalysis Algorithm Based on H265 Protocol

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1 MATEC Web of Conferences 5, ( 05) DOI: 0.05/ matecconf/ C Owned by the authors, publshed by EDP Scences, 05 Research of Vdeo Steganalyss Algorthm Based on H65 Protocol Kacheng Wu School of Mathematcs and Computer Scence, Janghan Unversty, Wuhan, Hube, Chna ABSTRACT: A new generaton of vdeo codng standard H65 has rased a publc concern by relevant scholars from all walks of lfe snce Aprl 00. Hgh-defnton vdeo s characterzed by huge data sze, complex encodng structure, hgh real-tme performance and so on. Therefore, to desgn encrypton and decrypton program accordng to the actual needs and combned wth applcaton scenaros s mperatve. Vdeo steganalyss algorthm s a key content of vdeo steganalyss (VSA), and VSA s a key technology of vdeo decrypton. Ths paper researches LSB matchng VSA based on H65 protocol wth the research background of 6 orgnal Vdeo sequences, t frstly extracts classfcaton features out from tranng samples as nput of SVM, and trans n SVM to obtan hgh-qualty category classfcaton model, and then tests whether there s suspcous nformaton n the vdeo sample. The expermental results show that VSA algorthm based on LSB matchng can be more practcal to obtan all frame embedded secret nformaton and carrer and vdeo of local frame embedded. In addton, VSA adopts the method of frame by frame wth a strong robustness n resstng attack n the correspondng tme doman. Keywords: vdeo steganalyss; LSB matchng; medan flter; characterstc; secret nformaton INTRODUCTION Wth the rapd development of hgh and new technology n the Internet and streamng meda, multmeda communcaton has become an ndspensable part for people to exchange nformaton. Recently, the Jont Collaboratve Team on Vdeo Codng (JCT-VC) proposes a new generaton of vdeo codng standard HEVC. The frst edton of HEVC standard was completed n January 03, whch was called as H65 by ITU-T. Steganography and steganalyss s one of themes of network nformaton confrontaton. Steganography embeds the secret nformaton nto the text, mage, vdeo and other dgtal carrer n dsguse to acheve unknown covert communcaton. Steganalyss s dvded nto actve and passve steganalyss. The passve steganalyss ams at determnng whether the vdeo contans the secret nformaton, whle the actve steganalyss ams at estmatng the secret nformaton tself, that s, calculatng the length of secret nformaton, hdng place or parameters used n the process of steganalyss. To explore the prncple of H65 vdeo steganalyss, ths paper proposes a vdeo steganalyss algorthm based on LSB matchng, so as to provde the theoretcal bass for the development of hgh-qualty multmeda communcaton technology. Many people have made efforts on the vdeo steganalyss. Wedong Zhong, et al. (0) proposed a real-tme vdeo steganalyss method, whch obtans an estmated value of vdeo frame by a sldng wndow wth the sze of L+, extracts the correspondng DCT and Markov characterstcs, and tests steganographc vdeo by the use of neural network, support vector machne and multple classfcaton methods []. Yfeng Sun, et al. (00) proposed a vdeo steganalyss detectng algorthm based on moton estmaton, whch researches the mpact of non-moton estmaton on embedded nformaton through the changes of error of mean square, fndng that the moton vector s senstve to steganography. The smaller the block s, the more senstve t s to the steganography []. Changyong Xu, et al. (00) analyzed the mpact of nose supermposton on temporal correlaton and spatal correlaton of the vdeo sequence, and proposed a steganalyss algorthm based on space-tme correlaton by the use of mpact of metrc steganography of four-drectonal dfference hstogram for each frame n the vdeo sequence on the spatal correlaton, and use of metrc steganography of adjacent frame dfference hstogram on the temporal correlaton [3]. Based on prevous research, ths paper proposes a vdeo LSB matchng steganalyss method through constructng regonal correlaton dagrams, and adopts ths method for vdeo steganalyss of H65 protocol, so as to provde theoretcal groundwork for the development of Chna s multmeda communcaton technology. OVERVIEW OF H65 CODEC People have an endless pursut on vdeo resoluton. Because of ths demand, the hgh-defnton vdeo ncreasngly emerges, but the butt jont of the current vdeo CODEC standard and hgh-defnton vdeo has dervaton [4]. To acheve seamless jont, the frst sesson of JCT-VC was held n Germany n Aprl 00. JCT-VC refers to a vdeo compresson standard organzaton. In ths sesson, a new generaton of vdeo 4 Artcle avalable at or

2 MATEC Web of Conferences Fgure. Framework of mxed vdeo encoder wth H65 standard Table. Lst of basc stuaton of H65 model confguraton scheme HE LC Tree structure codng unt Tree structure codng unt (Lumnance component 8 8 to 64 64) (Lumnance component 8 8 to 64 64) Predcton unt Predcton unt Tree structure transformaton unt (Three layers of depth) Tree structure transformaton unt (Two layers of depth) Converson unt s any consecutve three layers from 4 4 Converson unt s any consecutve three layers from 4 4 to 3 3 (always square) to 3 3 (always square) Intra-frame predcton (up to 35 knds of pattern) Intra-frame predcton (up to 35 knds of pattern) Brghtness pxel nterpolaton flter based on the dscrete Brghtness pxel nterpolaton flter based on the dscrete cosne transform cosne transform (/4 pxel 8-tab flter) (/4 pxel 8-tab flter) Brghtness pxel nterpolaton flter based on DCT transforform Brghtness pxel nterpolaton flter based on DCT trans- (/8 pxel 4-tap flter) (/8 pxel 4-tap flter) Encodng unt Skp mode, combned advanced moton Encodng unt Skp mode, combned advanced moton vector predcton of predcton unt vector predcton of predcton unt Self-adapton bnary arthmetc codng based on syntax Low complexty encodng(lcec) elements X (SBAC) Extended converson accuracy (4 bts) Increase the depth of the nternal bts (4 bts) Deblockng flter X X Deblockng flter Self-adapton recursve flter codng standard s proposed: HEVC (Hgh Effcency Vdeo Codng), namely, H65, and a predcton model (TMuC) [5],[6],[7] s establshed, then a new generaton of vdeo codng standard H65 emerges. The desgn of H65 standard ams at mprovng codng effcency and transmsson system ntegraton degree and data loss robustness, as well as enforceablty of parallel processng archtecture. Vdeo codng layer of H65 stll apples for the way of mxng performance of H64 vdeo compresson standard rules. As shown n Fgure, the framework of the mxed vdeo encoder wth H65 standard s as follows. H65 encoder contans two knds of encodng schemes [8] : the Hgh Effcency (HE) encodng scheme and the Low Complexty (LC) encodng scheme. The specfc confguraton stuaton of HE and LC encodng schemes s shown n Table p.

3 EMME 05 Fgure. Schematc dagram of MSU steganography prncple 3 VIDEO STEGANALYSIS METHOD 3. Steganography prncple Snce embeddng of secret nformaton s dstrbuted n dfferent frequency bands of vdeo, whch makes the secret nformaton n the ndvdual frame s dffcult to be detected. If the spread spectrum technology s used, t may make such secret nformaton sgnal amplfed and easy to detect nformaton. Three ways of embeddng the secret nformaton are as follows: v v x () v v x () v v e x (3) Where: v n the formula (), () and (3) represents coeffcent of the orgnal carrer mage; x represents the embedded nformaton sequence; represents the embedded strength coeffcent, and v represents the steganography coeffcent after embeddng. MSU Stego Vdeo [9] s a knd of software for addng and processng nformaton secret, whch s a knd of new software appearng n combnaton wth vdeo content n the current network. Such software has a hgh robustness n terms of the types of nformaton hdng, whch can bascally hde any type of fles n the AVI vdeo fles. Su, et al [0] construct a vdeo test frame sequence wth a specal sgnfcance by the use of a sngle gray value mage wth several frames, and then add secret nformaton for frame content n each frequency band of Vdeo by the use of MSU Vdeo steganography software, and also compare and analyze the dfferent mage sgnals between secret carrer mage and orgnal mage, so as to make clear of VSA mode features. MSU s a knd of mproved spread spectrum watermarkng algorthm. Such algorthm has a very strong robustness to some extent. It s bascally consstent wth dstrbuton rules of hdden nformaton data n a sngle gray value mage. In addton, such algorthm can also resst the mpact of varous standard vdeo compresson codng systems by ratonal selecton of the embedded parameters. MSU steganography prncple s shown n Fgure. 3. LSB matchng steganalyss algorthm In dfferent types of fles, that s, secrete nformaton, t s necessary to mbed the secret fles nto the target vdeo (carrer embed pont) n a way of secret nformaton, but both bts are often not compatble. Based on the above ncompatble stuaton, ths paper uses the LSB matchng steganalyss algorthm. Such algorthm adds and subtracts for pxel value at ths pont based on the stochastc crtera. By the use of matchng crtera based on related regons and to clarfy the correlaton T between the regons, such algorthm represents the correlaton, 55, 55 T through calculatng the dfference value and mean value between the pxel value and center pxel value n eght neghborhoods. The smaller the absolute value of such value s, the hgher the correlaton of the regon s. Its calculaton s shown n formula (4): j, j x u, v 9x (4) j 8 u v j T, Usng the above formula (4),, we can obtan regonal correlaton T and absolute value T n each pxel pont of vdeo for vdeo pretreatment, so that the vdeo consttuted by pxel ponts turns nto a vdeo frame consttuted by the value of regonal correlaton. Ths paper names t as RC (Regonal Correlaton) Dagram. Based on the above matchng crtera, the prncple of LSB matchng steganography can be descrbed by the formula (5). Where: x represents the pxel value of the embedded ponts; m represents the bts wth embedded secret nformaton; m represents the pxel value after embeddng secret nformaton: m x mod x mod & & x 55 T 0 x mod & & x 0 T 0 x x x m (5) x m Assume that the sze of vdeo frame s m n and defne that h d s a hstogram of RC Dagram. Its calculaton s shown n formula (6): p.3

4 h m n d T, j, j d (6) The regonal correlaton of the carrer vdeo frame s relatvely strong, whle LSB matchng steganography wll weaken such correlaton, so that the value of T s 0, and the number value of decreases. However, when T, the value wll have a correspondng ncrease. By the use of t,t classfcaton, Feature and Feature are shown n formula (7) as t,t, and the two features of the vdeo frame after steganography wll ncrease. The Feature 3 as shown n formula (8) that t 3 represents the centrod of the characterstc functon n the hstogram, N s DFT length, and Hk DEThd. To make the trend dsplayed by the hstogram of RC Dagram more obvous, there s a need n smoothng orgnal mage. The process of such smoothng s the elmnaton of burr n mage, such as settng up a low pass flter. t 0 h t hd d 0 h d 0 ; 6 h N / H k 0 k k k h 6 6 d 0 h h d d MATEC Web of Conferences (7) k 0 t 3 (8) N / H Features 4, 5, 6, 7, 8 and 9 are respectvely represented by M, d, r, U, e, 3d. Where: M represents the mean value of RC Dagram; d represents the varance; r represents the magntude of varance, whch s used to reflect the smoothness of RC Dagram; U represents the magntude of consstency; e represents the nformaton entropy of RC Dagram; 3d represents the thrd moment of RC Dagram, degree of skewness of the hstogram. The calculaton of above characterstc s shown n formula (9): M L d 0 h m d d d ; r n d L d d M e 3 L d 0 d d M ; U d h m n d 0 hd hd log m n m n L 3 hd m n d 0 L d 0 d h m n (9) The calculaton as two-dmensonal hstogram of RC Dagram s descrbed n formula (0). x takes four drectons: horzontal drecton m 0, n, vertcal drecton m, n 0, opposte angle and ant-opposte angle m, n. Where: h d, d represents the mean value of four drectons n two-dmensonal hstogram of RC Dagram. h h m n x d, d T, j d, T m, j n d j (0) d, d h d, d h d, d h d, d h d, d 4 h v Feature 0 and Feature can be calculated by the formula (0). Its values are represented by t0 and t. Its calculaton s shown n formula (): t 0 k t L h d0d 0 N N 0k0 N H k0k0 d, d h d, d k, k k k N L d0d0 H k, k, H d ad k, k DFT h d, d () 3.3 Classfcaton methods of selected model n support vector machne Ths paper adopts the support vector machne (SVM) showng sgnfcant advantages n dealng wth small samples, nonlnear and hgh-dmensonal pattern recognton problems as a classfer []. SVM s a learnng method based on statstcal theory. The core dea of ths method s explaned as follows: ) Based on mnmzaton of structural rsks, to control structural rsks of learnng machne through VC dmenson of the mnmum functon set, t may have strong generalzaton ablty. ) To obtan scentfc control of VC dmenson, t can be acheved by the use of maxmzng class nterval. 3) To avod solvng nonlnear mappng for solvng nner product, corng technology can be used to effectvely acheve the goals. SVM seeks for a functon based on functonal Mercer theorem, so as to make the nner product of the sample space correspond to nner product of the transformaton space. In order to get better test results, t s necessary for the classfer to select approprate estmated parameters. In tranng classfer of the support vector machne, the selecton of estmated parameters manly consders the followng three aspects: ) Compromse between the decson functon and tranng sample, that s, computatonal cost C. ) Selecton of mappng functon. 3) Kernel functon: Kx, x x x Ths paper carres out classfcaton of carrer and secret vdeo by the use of lbsvm classfcaton procedures provded by Ln ChhJen, and uses the radal bass functon as the kernel functon. Before classfcaton and accordng to the formula (), the features j j p.4

5 EMME 05 Fgure 3. Effect dagram of the feature classfcaton (Left - Drect extracton; rght - extracton after medan flterng) Table. Lst of classfcaton results of dfferent embeddng rates when all frames are embedded wth nformaton Sngle frame embeddng rate Accuracy False alarm rate Omsson rate 00.00% 99.95% 0.09% 0.00% 75.00% 99.93% 0.4% 0.00% 50.00% 99.9% 0.05% 0.4% 5.00% 99.88% 0.3% 0.00% 0.00% 97.44%.77%.35% Table 3. Lst of classfcaton results of dfferent embeddng rates when a part of frame s embedded Frame embeddng rate Sngle frame embeddng rate 80.00% 60.00% 40.00% 0.00% Accuracy Accuracy Accuracy Accuracy 00.00% 9.5% 97.5% 94.40% 90.0% 75.00% 00.00% 99.90% 99.87% 99.73% 50.00% 00.00% 99.78% 00.00% 99.3% 5.00% 00.00% 00.00% 99.6% 98.43% 0.00% 93.8% 9.76% 79.74% 66.88% Mean value of detecton rate 97.06% 97.99% 94.65% 90.9% used for classfcaton shall be normalzed n the range of [-, + ]: t t t 3mn t max t, t t max mn () mn max t n formula () respectvely represent the mnmum and maxmum values of Feature. 4 EXPERIMENTAL RESULT AND ANALYSIS The expermental object of ths paper s the vdeo sequence. A total of 6 orgnal vdeo sequences are selected, ncludng all knds of vdeo sequences of multple knds of velocty. The frame sze of each sequence s 00 frames. There are three types of frame sze, namely: 4CIF, CIF and QCIF. The experment adopts the trple cross valdaton, and randomly selects tranng samples and testng samples from the sample lbrary wth the proporton of :, and frstly selects the classfcaton features n the tranng samples as SVM nput, mplements tranng, and obtans optmal classfcaton model, and then uses the traned classfer to detect whether the vdeo sample to be tested contans suspcous nformaton. To get an accurate classfcaton results, the experment s repeated for 50 tmes accordng to the above process, and t obtans the average results. 4. Classfcaton results Medan flter can remove the dstncton between varous dsparate vdeo carrers that respectvely extracts features of the orgnal carrer and Stego Vdeo, and then compare wth features obtaned after the medan flterng. The orgnal feature s represented by,,, ; the feature after flterng s repre- t sented by,,, t. To dsplay the dfference target more ntutve, the research just verfes the same features 3, 4, 9 that extracted from three vdeo sequences. Each of them produces a three-dmensonal scatter dagram as shown n Fgure p.5

6 MATEC Web of Conferences ()Vdeo.ste-fan-cf.yuv;()Vdeo.moble_cf.yuv; (3) Vdeo 3.harbour_4cf.yuv. [XX] n Fgure 3 s XX Stego Vdeo, [XX] s XX carrer Vdeo. The effect of feature classfcaton n the fgure shows that drectly extracted three-dmensonal classfcaton features have partal overlappng n ste-fan-cf.yuv carrer Vdeo and harbour_4cf.yuv Stego Vdeo, whle three-dmensonal classfcaton features extracted from the medan flter can clearly dstngush carrer and Stego Vdeo. Thus, ths paper adopts the medan flter to remove the effect of dfferences between dfferent vdeo carrers, so as to serve for the hgh-qualty classfcaton performance. 4. Classfcaton performance analyss of frame embeddng n dfferent ways The secret nformaton s embedded n each frame n Vdeo sequences, wth the same embeddng rate of sngle frame. The embeddng rate s respectvely 00%, 75%, 50%, 5% and 0%. The test results of the Stego Vdeo are shown n Table. As shown n Table, the relatonshp trend curve of embeddng rate, accuracy, false alarm rate and omsson rate of sngle frame s shown n Fgure 4 and Fgure 5: The classfcaton effect of VSA on embeddng by all frames s very good, and the false alarm rate and omsson rate are relatvely low. Wth the decrease of the embeddng rate, the detecton rate also has a slght decrease. When the embeddng rate s 0%, a hgh detecton rate can also be ganed. A part of vdeo s randomly selected for embeddng. The number of secrete frames can be respectvely selected as 80%, 60%, 40% and 0%. The embeddng rate of sngle frame n each vdeo sequence can randomly select one from the embeddng rate p of 75%, 50%, 5% and 0%. Its classfcaton results are shown n Table 3. As shown n Table 3, when varous knds of embeddng rates are avalable n a vdeo sequence, LSB matchng VSA algorthm proposed n ths paper has a hgher detecton rate for the frame wth a hgher embeddng rate or non-steganography frame. For the frame wth a lower embeddng rate, t s prone to be msjudged as a vdeo frame wthout the secret nformaton. Wth the decrease of Stego Vdeo frame rato n the vdeo sequence, the msjudgment rate has a correspondng ncrease. But on the whole, LSB matchng VSA algorthm has a very hgh mean value of the detecton rate wth a hgh avalablty based on the stuaton of steganographc mxed embeddng of Vdeo LSB matchng. 5 CONCLUSION Fgure 4. Trend chart of the mpact of dfferent embeddng rates of sngle frame on accuracy Fgure 5. Trend dagram of the mpact of dfferent embeddng rates of sngle frame on false alarm rate and omsson rate Based on the summarzaton of H65 CODEC, ths paper sets forth the prncple of VSA, and proposes a LSB matchng VSA algorthm, and gves out a knd of computatonal algorthm of the classfcaton of features value, and carres out vdeo steganalyss analyss and experment on 6 orgnal vdeo sequences by the use of a classfcaton method used for selectng models by the support vector machne. The expermental result shows that: ) Analyss of the carrer and Stego Vdeo can be realzed through constructng RC Dagram and extractng relevant statstcal characterstcs, and removng the dfference of features n dfferent vdeo sequences by the medan flter. ) The classfcaton effect of LSB matchng VSA algorthm on embeddng by all frames s very good, and the false alarm rate and omsson rate are relatvely low. Wth the decrease of the embeddng rate, the detecton rate also has a slght decrease. When the embeddng rate s 0%, a hgh detecton rate can also be ganed. 3) When varous knds of embeddng rates are avalable n a vdeo sequence, LSB matchng VSA algorthm proposed n ths paper has a hgher detecton rate for the frame wth a hgher embeddng rate or non-steganography frame. 4) LSB matchng VSA algorthm has a very hgh mean value of the detecton rate wth a hgh avalablty based on the stuaton of steganographc mxed p.6

7 EMME 05 embeddng of Vdeo LSB matchng. In concluson, the vdeo steganalyss algorthm based on LSB matchng has a very strong robustness. The further research shall focus on LSB matchng steganography n the detecton feld and other vdeo steganalyss algorthms, so as to expand an applcable scope of such algorthm. REFERENCES [] Wedong Zhong, Junqng Wu, Gengru Wu & Habn Yang. 0. Invsble vdeo steganalyss method based on space-tme redundancy statstcs. Applcaton Research of Computer. 9 (0): [] Yfeng Sun & Fenln Lu. 00. Vdeo steganalyss method based on moton estmaton. Pattern recognton and artfcal ntellgence. 3 (6): [3] Changyong Xu & Xjan Png. 00. Vdeo steganalyss method based on space-tme correlaton. Journal of Chna Image and Graphcs. 5 (9): [4] Fang Guo. 03. Research of vdeo securty CODEC program based on standard H65. Chengdu: Southwest Jaotong Unversty. [5] Taoran Lu, Xaoan Lu, Qan Xu, Yunfe Zheng, Joel Sole, & Peng Yn. 0. A Vdeo Codng Analyzer for Next-Generaton Compresson Standards. IEEE Internatonal Conference on Consumer Electroncs (ICCE). [6] Ken McCann, Samsung, & ZetaCast. 00. Tool Experment : Evaluaton of TMuC Tools [R], ITU-T/ISO/IEC Jont Collaboratve Team on Vdeo Codng (JCT-VC) document JCTV-B3, July. [7] Frank Bossen. 00. AHG report: Software development and TMuC software techncal evaluaton [R], ITU-T/ISO/IEC Jont Collaboratve Team on Vdeo Codng (JCT-VC) document JCTVC-B003, July. [8] Sze V, Budagav M & Chandrakasan A Massvely Parallel SBAC [R], ITU-Y SGI 6/Q. 6VCEG-AL. [S..]: JCT-VC. [9] WANG We-hong, & FARID H Exposng dgtal forgeres n vdeo by detectng duplcaton. Proc of the 9th Workshop on Multmeda & Securty. New York: ACM Press: [0]SU Yu-tng, WANG L-l, & ZHANG Chun-tan A new dgtal vdeo steganalyss algorthm aganst moton vector. Proc of WCOM: []Rong L, Shwe Ye, & Zhongzh Sh. 00. SVM-KNN classfer: a new method of mprovng SVM classfcaton accuracy. Journal of Electroncs. 30 (5): p.7