Frame Selected Approach for Hiding Data within MPEG Video Using Bit Plane Complexity Segmentation

JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009, I SSN: 2151-9617 HTTPS://SITES.GOOGL E.COM/SITE/JOURNALOF COMPUTING/ 108 Frame Selected Approach for Hiding Dat a within MPEG V ideo Using Bit Plane Complexity Segment ation Hamid.A.Jalab, A.A Zaidan and B.B Zaidan Abstract --- Bit Plane Complexity Segmentation (BPC S) digital picture steganography is a tec hnique to hide data inside an image file. BP CS achieves high embedding rates with low dist ortion based on the t heory that noise-like r egions in an image’s bit-planes can be r eplaced w ith noise-like secret data without significant loss in image qualit y . . In this framework we will propose a collaborate approach fo r select frame for Hiding Data within MPEG Video Using Bit Plane Complexity Segmentation. This approach will invent high secure data hidden using select frame form MPEG Video and furthermore we will assign the well-bu ilt of the approach; during this review the author will answer the question why they used select frame steganography . In additional to the se curity issues we will use the digit al video as a cover to the data hidden. The reason behind opt the video cover in this approach is the huge amount of single frames image per sec which in turn overcome the problem of the data hiding quantity , as the experiment result shows the succe ss of the hidden data within select frame, extract data from the frames sequence. These function without affecting the quality of the video. Index Terms — S teganography , Hidden Data, BPCS, Frame Select ——————————  —————————— 1. I NTRODUCTION Steganography is the idea of hiding private or sensitive data or information within so mething that appears to be nothing out of the normal [1]. Steganography and cryptology are similar in the way that they both are used to protect important informat ion. The difference between the two is that Steganography involves hiding information so it appears that no information is hidden at all [2]. If a person views the digital object that the information is hidden inside, he or she will have no idea that there is any hidden information, therefore the person will not attempt to decrypt th e information, this is the main objective behind steganography [3]. Steganography comes from the Greek words Steganós (Covered) and Graptos (Writing), these days the sense of the word “Steganography” usually refers to information or a f ile that has been concealed inside a digital Picture, Video or Audio file [3],[4].What steganography technically does is to make use of human awareness; human senses are not trained to look for files that have information hidden inside of them[4]. ————————————— ——— • Dr. Hamid.A.Jalab- Senior Lecturer, Department of Computer S cience & Information Technology, University Malaya, Kuala Lumpur, Malaysia. • A. A. Zaidan – PhD Candidate on the Department of Electrical & Computer Engineering, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia. • B. B. Zaidan – PhD Candidate on the Department of Electrical & Computer Engineering / Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia. Although there are programs available that can do what is called Steganalysis (Detecting use of Steganography) [4]. The most common use of Steganography is to hide a file inside another file. When information or a file is hidden inside a carrier file, the data is usually encrypted with a password [4]. In this paper the researchers will focus on steganography on digital objects not Steganalysis, more specifically on digital video. 2. PREVIOUS WORKS In this section I will review the main Steganograp hy methods that have been used in video ste ganography and previous works been done based on those methods. Some if not most of these methods are also used by image steganography [5]. 2.1 Steganograp hy in Video Files Bas ed on the YCbCr Color S p ace YC bC r o r Y ' Cb Cr is a f am i ly of co lo r sp ac es us ed as a p a rt of the Color image pipeline in video and digital photography systems. YCbCr represe nts colors as a combination of three values: • Y - The luminosity (roughly the brightness). • Cb - the chrominance (roughly color) of the blue primary JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009, I SSN: 2151-9617 HTTPS://SITES.GOOGL E.COM/SITE/JOURNALOF COMPUTING/ 109 • Cr - the chrominance (rou ghly color) of the red primary (Green is achieved by using a combi nation of these three values). This technique is based on YCbCr. YCbC r space that can remove the correlation of R, G, and B in a given image, as less correlation between colors means less noticeable distortion. In this paper they concentrate on human video images, more specifically on human skin tones or colors for data hiding, note that human skin color can range from almost black to nearly colorless - appearing reddish white due to the blood vessels under the skin- in different people. The objective of this paper is to combat the use of forged passport documents or national identity cards, a security measure would be to embed individuals’ information in their photos [6]. 3. B IT P LA NE C OMPLEXITY S EGMENT A TION Bit Plane Complexity Segmentation steganography is a modern method of data hiding. Earlier methods of image steganography simply replaced the least significant bits of each pixel with hidden data [7]. This practice had very low embedding rates because visual defects rapidly develop as more significant bits are used. These defects are most noticeable in areas of homogenous color, where they usually appear as noise-like static. As more data is added, the noise [8]. Becomes more pronounced, until the image fades to unintelligible static. The degr adation can become obvious and severe with only 10 to 15 percent of the image replaced with secret data. On computer and television screens, the smallest division of color data is a pixel. In computer memory, each pixel is represented by a binary value. The more bits that are used to represent each value, the wider the range of colors are fo r each pixel. Typical amounts of bits per pixel (bpp) are 8, 24, and 32. With these binary pixel values, and knowledge of which part of the picture each one represents, we can construct bit planes [9]. A bit plane is a data stru cture made from all the bits of a certain significant position from each binary digit, with the special location preserved. In Fig 1. position (0, 0) from bit plane 2 is bit 2 from pixel (0, 0) in the image. BPCS addresses the embedding limit by wor king to disguise the visual artifacts that are produced by the steganographic process [10]. Optometri c studies have shown that the human visual system is very good at spotting anomalies in a reas of homogenous color, But less adept at seeing them in visually complex areas. When an image is deconstructed into bit planes, the complexity of each region can be measured. Areas of l ow complexity su ch as homogenous color or simple shapes appear as un iform areas with very few changes between one and zero [11]. Fig. 1. Image pixel location (0, 0) has the binary value 01001110. In these bit planes, black is a 0 and white is a 1. In the first bit plane in the figure, position (0, 0), there is a black zero. In the second bit plane, there is a white one, and so on down to the last bit plane. Fig. 2. Noise-like patch (a) and informative patch (b): (a) complexity 69, (b) complexity 29. Complex areas such as a picture of a forest wo uld appear as noise-like regions with many changes between one and zero. These random-seeming regions in each bit plane can then be replaced with hidden data, which is ideally also noise-like. Because it is difficult for the human eye to distinguish differences between the two noise-like areas, we are able to disgui se the changes to the image. Additionally, since complex areas of an image tend to be complex through many of their bit planes, much more data can be embedded with this technique than with those that are limited to only the lowest planes. In BPCS, the complexity of each subsection of a bit plane is defined as the number of non edge transitions from 1 to 0 and 0 to 1, both horizontally and vertically[12]. Thus the complexity of e ach section is not determined only by the number of one’s or z eros it contains. Generally, for any square of 2nx2n pixels, the maximum complexity is 2x2nx(2n-1) and the minimum is of course 0. Most versions of image BPCS use an 8 pixel square, where the maximum complexity is 112. In Figure 2, white represents a one and black a zero. Both squares, and ‘patches’, have the same number of ones and zeros, but very different complexities. This shows that one contains much more visual infor mat ion than the other. The complex patch (A) has very little visually informative information; therefore it can be replaced with secret date JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009, I SSN: 2151-9617 HTTPS://SITES.GOOGL E.COM/SITE/JOURNALOF COMPUTING/ 110 and with a very low effect on the image’s quality. However, if the more visually informative patch (B) was replaced, it would cause noise-like distortion of the definite edges and shapes. This technique works very well with natural images, as they tend to have many areas of high complexity. Images with many complex textur es and well shaded objects are usually having a high embedded data capacity [13]. BPCS works much less well with computer generated images and line art, as those classes of images tend to have large areas of uniformity and sharply defined border area s. With these types of images, there is very little complexity to exploit and any changes tend to generate very obvious artifacts [14]. This is one flaw BPCS shares with traditional steganography, though for slightly different reasons. Traditional steganography works poorly with computer generated pictures because the static distortion effect produced by embedding is very obvious in areas of homogenous color [15]. 4. S YSTEM O VERVIEW The main goal of our plan is to build a system progra m that is able to hide data in digital video files, more specifically in the images or frames extracted from the digital video file MPEG; as shown in Fig. 3. Fig. 3. Extracting Frames From Video File The main function in this framework is steganography this approach carry out the dreamily protection for the information and make the attackers dream on getting data back. The algorithm work as the chart shows below, where unsuspected carrier with the strongest for select frame building the characterist ic of our framework. Th e main function of the proposed approach is: • Read Frames. • Select Frame. • Hidden the Data. • Read Frames Sequence. • Extract the Data. Fig. 4. The Encode Algorithm The figure above showing the select frame with hidden operation this framework gi ve more flexibility to appoint the start point at which frame as well the end point, this new feature make the system more secure in term of avoiding discover the data hidden using the statistical Rewrite the Stego- Video End Apply Stego-Function File Cover not Ended Select Frame If video Size >0 Read Buffer If Frame Size >0 Start Read Frame s JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009, I SSN: 2151-9617 HTTPS://SITES.GOOGL E.COM/SITE/JOURNALOF COMPUTING/ 111 techniques. Fig.5. is the extraction operation with the select frame Fig. 5. Decoding Algorithm 5. EXPIREMENT AL RESUL TS Due to the difficulty of showing the result as a video stream on paper, the author prefers to display the resu lt on the frame of the digital video file along with histogram of each a single frame. The following here are extracted frames of a digital video file. Fig. 6. Shows the frames from the famous movie “The Godfather” before applying the algorithm, while Fig. 7. Shows the frame after applying the algorithm. We can see here that there are no much differences between the two sets of frames especially for human vision system. This can tell that the algorithm can be applied successfully on video frames also to verify the algorithm by the histogram, to see the divergences on the frames before and after hiding d ata. From the histogram for both single frames in Fig. 6 & 7, its clear there is no differences between the two sets before and after hiding data which prove that the algorithm successfully hid th e data into the frames without making a noticeable difference for the human vision system. Fig. 6. Fifteen image frames has taken from a much known movie. “Godfather” be fore any hidden operation, the first frame under the histogram also the three channels on RGB has been separated for more accuracy on the test. Start Extract Function Write the data If video Size >0 Extract done Read frames Sequence End JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009, I SSN: 2151-9617 HTTPS://SITES.GOOGL E.COM/SITE/JOURNALOF COMPUTING/ 112 Fig. 7. Fifteen image frames has taken from a very known movie “Godfather” after hidden operation, the first frame under the histogram also the three channels on RGB has been separated, 6. CONCLUSION In this paper, a new Approach of high secure vid eo steganography has been invented. The basis of this method is use the digital video as separate frames and select frame to hides the information inside. As the experiment result shows the success of the hidden data within select frame, extract data from the frames sequence, these functions without affecting the quality of the video. This framework overcome the defeat of the limitation of steganography app roach by invited the biggest size cover file among the multimedia file which is the video. In the video steganography we have a flexibility of make a selective frame st eganography to higher the security of the system or using the whole vid eo too high a huge amount of data hidden. Due the security issues the author has select frame from the whole frames which is in buffer, this idea make to guarantee the protection of data. ACKNOWLEDGEMENT This work was supported in part by the University of Malaya, Kuala Lumpur Malaysia. REFERENCES [1] A.A.Zaidan, B.B.Zaidan , “Novel Approach for High Secure Da ta Hidden in MPEG Video Usin g Public Key Infr astructure”, Internation al Journal of Computer and Network Security, Vol.1, No.1, P.P 71-76, 30 October, Vienna, Austria, 2009. (Journal paper). [2] Alaa Taq a, A.A Zaidan, B.B Za idan ,“Ne w Framework fo r High S ecure Data Hidd en in the MPE G Using AES Encrypt ion Algorit hm”, International Journal o f Computer and Electrical Engi neering (IJCEE),Vol.1 ,No.5, ISSN: 1793-8198, pp.589-595 , Singapore.,December (2009) . (Journal paper). [3] Mohamed El sadig El tahir, La iha Mat K iah, B.B. Zaidan a nd A.A.Z aidan," High Rate Video S treaming St eganog raphy", International Conference o n Information Manageme nt and Engineering (ICIME09), Session 10, P.P 550-553, 2009 . ( Conf erence proceeding ). [4] Fazida.O thman, Miss.Laiha .Maktom, A.Y .Taqa, B. B.Zaidan, A.A.Zaidan, "An Extensive Empirical Study for the Impact of Incre a sing Data Hidden on the Images Texture", Intern ational Conferen ce on Future Comp uter and Co mmunicatio n (ICFCC 09) , Session 7, P.P 477-481, 2009. (Conference proceeding ). [5] Hamid.A.Jalab, A.A Zaidan, B.B Zai dan, “New Design for Information Hiding with in St eganography U sing Dist ortion Techniq ues”, Interna tional Journal of Engi neerin g and Tec hnology (IJET )), Vol 2 , No. 1, ISSN: 1793-8236, Singapore , Feb (2010), (Pending publication). [6] A.A.Za idan, B.B. Zaidan, Hamid.A.J a lab,“ A Ne w System for Hidi ng Data within (Unused Area Two + Image Page) of Portable Executable File using Statistical Technique and Advance Encrypti on Standared “, International Journal of Co mputer Theory and Engineering (IJCTE), 2010, VOL 2, NO 2, IS SN:1793-8201,Singapore,2009, (Journal paper). [7] Stallings, William, Network Security Essentials, applica tions and Standards p.75, Pearson Edu cation, Inch, 2007.( Technical report with report number) [8] Yeob , Chan & Farnham , Ti m , (2003). Secure M-Commerce with WPKI, Toshiba Research Europe Limited, Toshiba Telecommunication Research Laboratory, available at: http://www.iris.re.kr/iwap01/program/download/g0 7_paper.pdf, (accessed May 4, 2008). [9] K u h n , D . R i c h a r d & H u , V i n c e n t C . & P o l k , W . T i m o t h y & C h a n g , Shu-Jen , (2001). Introdu ction to Public Key Techn ology and the Federal PKI Infr astruct ure, Nat ional inst itute of standars and technology,(NIST),available, from:http://csrc.nist.gov/ publications/nis tpubs/800-32/sp800-32.pdf. (Accessed April 28, 2008). [10] E. Kawaguchi, M. Niimi. “ Modeling Digital image in to informative and Nois e-Like R egions by a C omplex ity Meas ure”, 7th European -Japanese Conference on Information Modeling and knowledge Bases, 1997. ( Conference proceeding ) [11] M. Ni imi , H. Nod a, E. K awa gu ch i. “A Ste gan og rap hy B as ed on R eg io n Segmentatio n by Using Co mplexity Measure” . Trans. Of,IEICE, Vol . J81-D-II, No. 6, pp. 1132-1140, 1998.( T ran sact ion , j our nal ) [12] R. Ouellette, H. Noda , M. Niimi, E. Kawaguchi. “Top ological Ordered Color Table f or BPCS St eganog rapy Usin g Indexed Color I mages ”, IPSJ Journal, 2000, (Journal paper ). JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009, I SSN: 2151-9617 HTTPS://SITES.GOOGL E.COM/SITE/JOURNALOF COMPUTING/ 113 [13] E. Kawaguc hi, R.O. Easo n. “Princi pal and A pplication s of BPCS Steganography”, Proc of SPIE, Vol. 3528, pp. 464-473, 1998.. (Conference proceeding) [14] J. M. Shapiro. “ Embedded I mage Coding Usi ng Zerotrees of Wavele t Coefficients”, IEEE Transactions on Signal Processing, pp.3445-62, 1993. (Transactio n, journal ). [15] A. Moffat, R. Neal, I. H. Witten. “Arithmetic Coding Revisited”, ACM Transactions on Information Systems, 16(3):256-294, 1998., (Journak paper) Dr.Hamid.A.Jalab: Received his B.Sc degree from University of Technology, Baghdad, Iraq. MSc & Ph.D degrees from Odessa Polytechnic National State Univers ity 1987 and 1991, respective ly. Presently, Visiting Senior Lecturer of Computer S ystem and Technology, Faculty of Computer Science and Inform ation Technology, Universit y of Malaya, Malaysia. His areas of interest include ne ural networks and cryptography. Aos Alaa Zaidan : He obtained his 1st Class Bachelor degree in Computer E ngineering from univ ersity of Technology / Bag hdad follo wed by master in data communication and compute r network from University of Malaya. He led or memb er for many funded research projects and He has publis hed more than 45 papers at various international and n ational conferences and j ournals, he has done man y projects on Stega nography for data hidden through different mu ltimedia carriers image, video, audio, text, and non multime dia carrier unused ar ea within exe. File, Cryptography and Stego-A nalysis systems, currently he is working on th e multi module for Steganography, Developmen t & Implement a novel Skin Detector for increase the reliabil ity. He is PhD Candidate on the Department of Electric al & Computer Engineer ing / Faculty of Engineering / Multimedi a Universit y / Cyberjaya, Malaysia. He is members IAENG, CSTA, WASET, and IACSIT. He is reviewer in the (IJSIS, IJCSNS, IJCSN, IJCSE and IJCIIS). Bilal Bahaa Zaidan: He obtained his ba chelor degree in Mathematics and Computer A pplication from Saddam University/Baghdad follo wed by master from Department of Computer System & T echnology Departm ent Faculty of Computer Science and Information Techno logy/University of Malaya /Kuala Lump ur/Malaysia, He led or member for many funded research projects an d He has publish ed more than 45 papers at various i nternational and nati onal conferences and journals. His research i nterest on Steganograp hy & Cryptography with his gr oup he has published man y papers on data hidden through different multimed ia carriers such as image, video, audio, text, and non m ultimedia careers such as unused area within exe. File, he has done pro jects on Stego-Analysis systems, current ly he is working on mul ti module for Steganography, and he is PhD candidate on the Department of Electrical & Com puter En gineering / Faculty of Engineering / Multimedia U niversity / Cyberjaya, Mal aysia, He is members IAENG, CSTA, WASET, and IACSIT . He is reviewer in the (IJSIS, IJCSNS, IJCSN, IJCSE and IJCIIS).