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A survey on digital data hiding schemes: principals, algorithms, and applications
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This paper investigates digital data hiding schemes. The concept of information hiding will be explained at first, and its traits, requirements, and applications will be described subsequently. In order to design a digital data hiding system, one should first become familiar with the concepts and criteria of information hiding. Having knowledge about the host signal, which may be audio, image, or video and the final receiver, which is Human Auditory System (HAS) or Human Visual System (HVS), is also beneficial. For the speech/audio case, HAS will be briefly reviewed to find out how to make the most of its weaknesses for embedding as much data as possible. The same discussion also holds for the image watermarking. Although several audio and image data hiding schemes have been proposed so far, they can be divided into a few categories. Hence, conventional schemes along with their recently published extensions are introduced. Besides, a general comparison is made among these methods leading researchers/designers to choose the appropriate schemes based on their applications. Regarding the old scenario of the prisonerwarden and the evil intention of the warden to eavesdrop and/or destroy the data that Alice sends to Bob, there are both intentional and unintentional attacks to digital information hiding systems, which have the same effect based on our definition. These attacks can also be considered for testing the performance or benchmarking, of the watermarking algorithm. They are also known as steganalysis methods which will be discussed at the end of the paper.
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M. A.
Akhaee
Iran
akhaee@ut.ac.ir


F.
Marvasti
Iran
marvasti@sharif.edu
Data hiding
watermarking
Capacity
robustness
Security
Steganalysis
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Pearlman, Steganalysis of additivenoise modelable information hiding, in Society of PhotoOptical Instrumentation Engineers (SPIE) Conf., vol. 5020, pp. 131142, 2003. ##[140] A. D. Ker, Steganalysis of lsb matching in grayscale images, IEEE Signal Process. Lett., vol. 12, no. 6, pp. 441  444, Jun. 2005. ##[141] X. Li, T. Zeng, and B. Yang, Detecting lsb matching by applying calibration technique for difference image, Proc. of the 10th ACM workshop on Multimedia and security, pp. 133138, 2008. ##[142] T. Pevny, P. Bas, and J. Fridrich, Steganalysis by subtractive pixel adjacency matrix, IEEE Trans. on Information Forensics and Security, vol. 5, no. 2, pp. 215 224, Jun. 2010. ##[143] H. Farid, Detecting hidden messages using higherorder statistical models, International Conference on Image Processing, vol. 2, pp. 905908, 2002. ##[144] I. Avcibas, N. Memon, and B. Sankur, Steganalysis using image quality metrics, IEEE Transactions on Image Process., vol. 12, no. 2, pp. 221229, 2003. ##[145] F. Huang, B. Li, and J. Huang, Attack lsb matching steganography by counting alteration rate of the number of neighborhood gray levels, Proc. of International Conference on Image Processing, pp. 401404, 2007. ##[146] Y. Wang and P. Moulin, Optimized feature extraction for learningbased image steganalysis, IEEE Trans. on Info. Forensics and Security, vol. 2, no. 1, pp. 31 45, Mar. 2007. ##[147] T. Pevny, P. Bas, and J. Fridrich, Steganalysis by subtractive pixel adjacency matrix, IEEE Transactions on Info. Forensics and Security, vol. 5, no. 2, pp. 215224, 2010. ##[148] Y. Q. Shi, C. Chen, and W. Chen, A markov process based approach to effective attacking jpeg steganography, in Information Hiding. Springer, pp. 249264, 2007. ##[149] C. Chen and Y. Shi, Jpeg image steganalysis utilizing both intrablock and interblock correlations, Proc. of International Symposium on Circuits and Systems, pp. 30293032, 2008. ##[150] T. Pevny and J. Fridrich, Merging Markov and DCT features for multiclass JPEG steganalysis, Proceedings SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, vol. 3, pp. 11171126 2007. ##[151] J. Kodovsky, J. Fridrich, Steganalysis in high dimensions: fusing classifiers built on random subspaces, Proc. of SPIE Media Watermarking, Security, and Forensics III, pp. 2326, 2011. ##[152] Y. Shi, G. Xuan, D. Zou, J. Gao, C. Yang, Z. Zhang, P. Chai, W. Chen, and C. Chen, Image steganalysis based on moments of characteristic functions using wavelet decomposition, predictionerror image, and neural network, Proc. of International Conference on Multimedia and Expo(ICME), 2005. ##[153] C. C. Chang and C. J. Lin, Libsvm: a library for support vector machines, 2001, software available at http://www.csie.ntu.edu.tw/cjlin/libsvm. ##[154] J. Kodovsky, J. Fridrich, and V. Holub, Ensemble classifiers for steganalysis of digital media, IEEE Trans. on Info. Forensics and Security, vol. 7, no. 2, pp. 432444, 2012. ##]
Design and formal verification of DZMBE+
2
2
In this paper, a new broadcast encryption scheme is presented based on threshold secret sharing and secure multiparty computation. This scheme is maintained to be dynamic in that a broadcaster can broadcast a message to any of the dynamic groups of users in the system and it is also fair in the sense that no cheater is able to gain an unfair advantage over other users. Another important feature of our scheme is collusion resistance. Using secure multiparty computation, a traitor needs k cooperators in order to create a decryption machine. The broadcaster can choose the value of k as he decides to make a tradeoff between communication complexity and collusion resistance. Comparison with other Broadcast Encryption schemes indicates enhanced performance and complexity on the part of the proposed scheme (in terms of message encryption and decryption, key storage requirements, and ciphertext size) relative to similar schemes. In addition, the scheme is modeled using applied pi calculus and its security is verified by means of an automated verification tool, i.e., ProVerif.
1

37
53


M.
Soodkhah Mohammadi
Iran
xemailpro@yahoo.co.uk


A.
Ghaemi Bafghi
Iran
ghaemib@ferdowsi.um.ac.ir
Broadcast Encryption
Secure Multiparty Computation
Threshold Secret Sharing
Formal Methods
Applied pi Calculus
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Provably secure and efficient identitybased key agreement protocol for independent PKGs using ECC
2
2
Key agreement protocols are essential for secure communications in open and distributed environments. Recently, identitybased key agreement protocols have been increasingly researched because of the simplicity of public key management. The basic idea behind an identitybased cryptosystem is that a public key is the identity (an arbitrary string) of a user, and the corresponding private key is generated by a trusted Private Key Generator (PKG). However, it is unrealistic to assume that a single PKG will be responsible for issuing private keys to members of different organizations or a largescale nation. Hence, it is needed to consider multiple PKG environments with different system parameters. In this paper, we propose an identitybased key agreement protocol among users of different networks with independent PKGs, which makes use of elliptic curves. We prove the security of the proposed protocol in the random oracle model and show that all security attributes are satisfied. We also demonstrate a comparison between our protocol and some related protocols in terms of the communication costs and the execution time. The results show that the execution time of our protocol is less than 10%, and its communication costs are about 50% of the competitor protocols.
1

55
70


M.
Sabzinejad Farash
sabzinejad@tmu.ac.ir
sabzinejad@tmu.ac.ir
Iran
sabzinejad@tmu.ac.ir


M.
Ahmadian Attari
Iran
mahmoud@eetd.kntu.ac.ir
IdentityBased Cryptography
Key Agreement Protocol
Elliptic Curve Cryptography
Random Oracle Model
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Lee, C.N.Wu, C.C.Wang, "Authenticated multiple key exchange protocols based on elliptic curves and bilinear pairings," Computers & Electrical Engineering, vol. 34, no. 1, 2008, pp. 1220. ##[7] D.L. Vo, H. Lee, C.Y. Yeun, K. Kim, "Enhancements of authenticated multiple key exchange protocol based on bilinear pairings," Computers & Electrical Engineering, vol. 36, no. 1, 2009, pp. 155159. ##[8] M.S. Farash, M. Bayat, M.A. Attari, "Vulnerability of two multiplekey agreement protocols," Computers & Electrical Engineering, vol. 37, no. 2, 2011, pp. 199204. ##[9] M.S. Farash, M. Gardeshi, M. Bayat, "Security Enhancement of a multiplekey exchange protocol based on bilinear pairings," 6th International ISC Conference on Information Security and Cryptology (ISCISC2009), 2009, pp. 175182. ##[10] Q. Cheng, C. Ma, "Analysis and improvement of an authenticated multiple key exchange protocol," Computers & Electrical Engineering, vol. 37, no. 2, 2011, pp. 187190. ##[11] L. Ni, G. Chen, J. Li, Y. Hao, "Strongly secure identitybased authenticated key agreement protocols," Computers & Electrical Engineering, vol. 37, no. 2, 2011, pp. 205217. ##[12] M. Holbl, T. Welzer, B. Brumen, "An improved twoparty identitybased authenticated key agreement protocol using pairings," Journal ##of Computer and System Sciences, vol. 78, no. 1, 2012, pp. 142150. ##[13] D. He, "An efficient remote user authentication and key agreement protocol for mobile clientserver environment from pairings," Ad Hoc Networks, vol. 10, no. 6, 2012, pp. 10091016. ##[14] Z. Zhang, L. Zhu, L. Liao, and M.Wang, "Computationally sound symbolic security reduction analysis of the group key exchange protocols using bilinear pairings," Information Sciences, vol. 209, 2012, pp. 93112. ##[15] Y. Chuang, Y. Tseng, "Towards generalized IDbased user authentication for mobile multiserver environment," International Journal of Communication Systems, vol. 25, no. 4, 2012, pp. 447460. ##[16] K. Shim, "A roundoptimal threeparty IDbased authenticated key agreement protocol, "Information Sciences, vol. 186, 2012, pp. 239248. ##[17] K. Shim, "Cryptanalysis of Two IdentityBased Authenticated Key Agreement Protocols," IEEE Communications Letters, vol. 16, no. 4, 2012, pp. 554556. ##[18] L. Ni, G. Chen, and J. Li, "Escrowable identitybased authenticated key agreement protocol with strong security," Computers and Mathematics with Applications, 2012, ##doi:10.1016/j.camwa.2012.01.041. ##[19] M.S. Farash, M.A. Attari, "A new improved and efficient authenticated multiplekey agreement protocol based on bilinear pairings," Computers & Electrical Engineering, 2012, http://dx.doi. ##org/10.1016/j.compeleceng.2012.09.004. ##[20] L. Chen, Z. Cheng, N.P. Smart, "Identitybased key agreement protocols from pairings," International Journal of Information Security, vol. 6, no. 4, 2007, pp. 213241. ##[21] P. Barreto, H. Kim, B. Lynn, M. Scott, "Efficient algorithms for pairingbased cryptosystems," Proc. CRYPTO 2002, LNCS, vol. 2442, 2002, pp. 354368, Springer. ##[22] P. Barreto, B. Lynn, M. Scott, "On the selection of pairingfriendly groups," Selected Areas in Cryptography (SAC 2003), LNCS, vol. 3006, 2003, pp. 1725. ##[23] D. He, J. Chen, J. Hu, "An IDbased client authentication with key agreement protocol for mobile clientserver environment on ECC with provable security. Information Fusion, vol. 13, no. 3, 2012, pp. 223230. ##[24] W. Han and Z. Zhu, "An IDbased mutual authentication with key agreement protocol for multiserver environment on elliptic curve cryptosystem," International Journal of Communication Systems, 2012, DOI: 10.1002/dac.2405. ##[25] S. H. Islam, G. P. Biswas, "A more efficient and secure IDbased remote mutual authentication with key agreement scheme for mobile devices on elliptic curve cryptosystem," The Journal of Systems and Software, vol. 84, no. 11, 2011, pp. 18921898. ##[26] R.W. Zhu, G. Yang, D.S. Wong, "An efficient identitybased key exchange protocol with KGS forward secrecy for lowpower devices, Theor. Comput. Sci. vol. 9, no. 378, 2007, pp. 198207. ##[27] X. Cao, W. Kou, Y. Yu, R. Sun, "Identitybased authentication key agreement protocols without bilinear pairings," IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, vol. E91A, no. 12, 2008, pp. 38333836. ##[28] X. Cao, W. Kou, Y. Yu, R. Sun, "Identitybased authentication key agreement protocols without bilinear pairings," Information Sciences, vol. 180, 2010, pp. 28952903. ##[29] S.K. Hafizul Islam, G.P. Biswas, "An improved pairingfree identitybased authenticated key agreement protocol based on ECC," International Conference on Communication Technology and System Design 2011, Procedia Engineering, vol. 30, 2012, pp. 499507. ##[30] H. Lee, D. Kim, S. Kim, H. Oh, "Identitybased Key Agreement Protocols in a Multiple PKG Environment," Proc. of the Int. Conf. on Computational Science and Its Applications, ICCSA 2005. LNCS, vol. 3483, 2005, pp. 877886. ##[31] S. Kim, H. Lee, H. Oh, "Enhanced IDBased Authenticated Key Agreement Protocols for a Multiple Independent PKG Environment," Proc. Of ICICS 2005, LNCS, vol. 3783, 2005, pp. 323335. ##[32] M.S. Farash, M.A. Attari, "An IDBased Key Agreement Protocol Based on ECC Among Users of Separate Networks," 9th International ISC Conference on Information Security and Cryptology (ISCISC2012), September 2012, Tabriz, Iran. ##[33] I.F. Blake, G. Seroussi and N.P. Smart, "Advances Elliptic Curves in Cryptography," London Mathematical Society Lecture Note Series. 317, United States of America by Cambridge University Press, New York, 2005. ##[34] A. Joux and K. Nguyen, "Separating Decision DiffieHellman from DiffieHellman in cryptographic groups," Journal of Cryptology, no. 16, 2003, pp. 239248. ##[35] S. BlakeWilson, A. Menezes, Authenticated DiffieHellman key agreement protocols, in: Proc. SAC98, LNCS vol. 1556, 1999, pp. 339 361. ##[36] C. Boyd, A. Mathuria, Protocols for Authentication and Key Establishment. SpringerVerlag, June 2003. ##[37] C. Kudla, "Special signature schemes and key agreement protocols," Ph.D. Thesis, Royal Holloway University of London, 2006. ##[38] M. Bellare and Ph. Rogaway, "Entity Authentication and Key Distribution," In Advances in CryptologyCRYPTO93, LNCS, vol. 773, 1993, pp. 232249. ##[39] M. Bellare and Ph. Rogaway, "Provably secure session key distribution: the three party case," In Proc. of the 27th Annual ACM Symposium on Theory of ComputingSTOC'95, 1995, pp. 5766. ##[40] S. BlakeWilson, D. Johnson, A. Menezes, "Key agreement protocols and their security analysis," Proc. of the 6th IMA International Conference on Cryptography and Coding, 1997, pp. 3045. ##[41] L. Chen, C. Kudla, "Identity based authenticated key agreement from pairings," In IEEE Computer Security Foundations Workshop, 2003, pp. 219233. ##[42] K. Choo, C. Boyd, Y. Hitchcock, "On session key construction in provablysecure key establishment protocols: revisiting Chen & Kudla (2003) and McCullagh & Barreto (2005) IDbased protocols," In Mycrypt'05, LNCS, vol. 3715, 2005, pp. 116131. ##[43] Z. Cheng, M. Nistazakis, R. Comley, L. Vasiu, "On the indistinguishabilitybased security model of key agreement protocolssimple cases," Cryptology ePrint Archive, Report 2005/129. ##[44] C. Kudla, K. Paterson,"Modular security proofs for key agreement protocols," In Advances in CryptologyAsiacrypt'05, LNCS, vol. 378, 2005, pp. 549565. ##[45] Y. Wang, "Efficient identitybased and authenticated key agreement protocol," Cryptology ePrint Archive, Report 2005/108. ##[46] Shamus Software Ltd., Miracl library. http://www.shamus.ie/index.php?page=home. ##]
DyVSoR: dynamic malware detection based on extracting patterns from value sets of registers
2
2
To control the exponential growth of malware files, security analysts pursue dynamic approaches that automatically identify and analyze malicious software samples. Obfuscation and polymorphism employed by malwares make it difficult for signaturebased systems to detect sophisticated malware files. The dynamic analysis or runtime behavior provides a better technique to identify the threat. In this paper, a dynamic approach is proposed in order to extract features from binaries. The runtime behavior of the binary files were found and recorded using a homemade tool that provides a controlled environment. The approach based on DyVSoR assumes that the runtime behavior of each binary can be represented by the values of registers. A method to compute the similarity between two binaries based on the value sets of the registers is presented. Hence, the values are traced before and after invoked API calls in each binary and mapped to some vectors. To detect an unknown file, it is enough to compare it with dataset binaries by computing the distance between registers, content of this file and all binaries. This method could detect malicious samples with 96.1% accuracy and 4% false positive rate. The list of execution traces and the dataset are reachable at: http://home.shirazu.ac.ir/˷ sami/malware
1

71
82


M.
Ghiasi
Iran
mhbbgh@gmail.com


A.
Sami
Iran
asami@ieee.org


Z.
Salehi
Iran
zsalehi@cse.shirazu.ac.ir
Malware Detection
API Call
Dynamic Analysis
CPU Register Values
x86 Registers Values
[[1] M. Christodorescu, S. Jha, S. A. Seshia, D. Song, and R. E. Bryant, "SemanticsAware Malware Detection," IEEE Symposium on Security and Privacy (S&P05), Washington. DC. USA, pp. 3246, 2005. ##[2] Symantec Corp, "Symantec Global Internet Security Threat Report," Vol. 7, 2008. ##[3] PandaLabs, "Pandalabs annual malware report 2009," 2010. ##[4] K. Kim, and B. R. Moon, "Malware detection based on dependency graph using hybrid genetic algorithm," Proceedings of the 12th Annual Conf. on Genetic and Evolutionary Computation, ACM. USA. , pp. 12111218, July 2010. ##[5] McAfee Labs, "McAfee Threats Report: Fourth Quarter 2010," McAfee Inc., Santa Clara. California, 2010. ##[6] X. Hu, "LargeScale Malware Analysis, Detection, and Signature Generation," A dissertation for the degree of Doctor of Philosophy, University of Michigan, Ann Arbor. Michigan. United States, 2011. ##[7] P. Wood, M. Nisbet, G. Egan, N. Johnston, K. Haley, B. Krishnappa, T. K. Tran, I. Asrar, O. Cox, S. Hittel, et al., "Symantec Internet Security Threat Report Trends for 2011," Vol. 17, Symantec Corporation, 2012. ##[8] PandaLabs, "Pandalabs annual malware report 2011," 2012. ##[9] Panda Security, "PandaLabs Annual Report 2012," 2013. ##[10] Sophos, "Security threat report 2013 New Platforms and Changing Threats," Sophos Ltd., Boston, USA, 2013. ##[11] Macafee Labs, "McAfee Threats Report: Fourth Quarter 2012," McAfee Inc, 2013. ##[12] Symantec Corporation, "Internet Security Threat Report 2013," Vol. 18, 2013. ##[13] Sophos, "Security threat report 2011," Sophos Ltd., Boston, USA, January 2011. ##[14] Symantec Corporation, "The Shamoon Attacks," [Online]. Available electronically at http://www.symantec.com/connect/blogs/shamoonattacks. 2012. ##[15] Norton by Symantec, "2012 Norton Cybercrime Report," 2012. ##[16] A. E. Ammar, A. M. Mohd, and H. Ahmed, "Malware Detection Based on Hybrid Signature Behaviors Application Programming Interface Call Graph," American J of Applied Sciences, United States, vol. 3, pp. 283288, 2012. ##[17] L. Bohne, "Pandoras Bochs: Automatic Unpacking of Malware," Diploma Thesis, University of Mannheim, January 2008. ##[18] M. Egele, T. Scholte, E. Kirda, and C. Kruegel, "A survey on automated dynamic malware analysis techniques and tools," ACM Computing Surveys (CSUR) J., Vol. 44, ACM. New York. USA, pp. 149, February 2012. ##[19] S. M. Abdulalla, L. M. Kiah, and O. Zakariam, "A biological Model to Improve PE Malware Detection: Review," Int. J. of Physical Sciences, vol. 5, pp. 22362247, 2010. ##[20] K. M. Goertzel, "Tools on Anti Malware," Technical Information Center, 2009. ##[21] Li. Shengying, "A survey on tools for binary code analysis," Stony Brook University, August 2004. ##[22] M. Bailey, J. Oberheide, J. Andersen, Z. Mao, F. Jahanian, and J. Nazario, "Automated Classification and Analysis of Internet Malware," In Proceedings of Symposium on Recent Advances in Intrusion Detection (RAID07), pp. 178197, 2007. ##[23] M. Yahyazadeh, and M. Abadi, "BotOnus: An Online Unsupervised Method for Botnet Detection," The ISC Int. J. of Information Security (ISeCure), vol. 4, pp. 5162, January 2012. ##[24] P. Li, L. Liu, D. Gao, and M. K. Reiter, "On challenges in evaluating malware clustering," In Proceedings of the 13th Int. Conf. on Recent advances in intrusion detection (RAID10), Berlin. Heidelberg, pp. 238255, 2010. ##[25] Z. Salehi, M. Ghiasi, and A. Sami, "Malware Detection Preserving API Function Calls and Their Standard Function Calling Notation," In ##Proceeding of 16th CSI Symposium on Artificial Intelligence and Signal Processing (AISP 2012), Shiraz, Iran, 2012. ##[26] H. Zhao, M. Xu, N. Zheng, J. Yao, and Q. Ho, "Malicious executable classification based on behavioral factor analysis," In Proceeding Int. Conf. on eEducation, eBusiness, eManagement and eLearning (IC4E 2010), Sanya. China, pp. 502 506, 2010. ##[27] F. Ahmed, H. Hameed, M. Z. Shafiq, and M. Farooq, "Using spatiotemporal information in api calls with machine learning algorithms for malware detection," In Proceeding Second ACM workshop on Security and artificial intelligence (AISec 09), New York, USA, pp. 5562, 2009. ##[28] R. Tian, R. Islam, and L. Batten, "Differentiating Malware from Cleanware Using Behavioral Analysis," In Proceeding Fifth Int. Conf. on Malicious and Unwanted Software (MALWARE 2010), Nancy, France, pp. 2330, 2010. ##[29] F. Leder, B. Steinbock, and P. Martini, "Classification and detection of metamorphic malware using value set analysis," In Proceeding Fourth Int. Conf. on Malicious and Unwanted Software (MALWARE 2009), pp. 3946, 2009. ##[30] V. S. Sathyanarayan, P. Kohli, and B. Bruhadeshwar, "Signature Generation and Detection of Malware Families," In Information Security and Privacy 13th Australasian Conf. (ACISP 2008),Wollongong, Australia, pp. 336349, July 2008. ##[31] R. Moskovitch, D. Stopel, C. Feher, N. Nissim, and Y. Elovici, "Unknown Malcode Detection via Text Categorization and the Imbalance Problem," Intelligence and Security Informatics (ISI 2008), Taipei. Taiwan, pp. 156181, 2008. ##[32] I. Santos, F. Brezo, J. Nieves, Y. K. Penya, B. Sanz, C. Laorden, and P. G. Bringas, "Idea: Opcodesequencebased malware detection," In Engineering Secure Software and Systems Second Int. Symposium (ESSoS 2010), Pisa. Italy, pp. 3543, February 2010. ##[33] R. Tian, L. M. Batten, and S. C. Versteeg, "Function Length as a Tool for Malware Classification," In Proceedings of the 3rd Int. Conf. on Malicious and Unwanted Software (Malware 2008), pp. 6976, 2008. ##[34] R. Tian, L. Batten, R. Islam, and S. Versteeg, "an Automated Classification System based on the Strings of Trojan and Virus Families," In Proceedings of the 4th Int. Conf. on Malicious and Unwanted Software (MALWARE 2009), Quebec. Canada, pp. 2330, October 2009. ##[35] Y. Ye, T. Li, Q. Jiang, and Y. Wang, "CIMDS: Adapting Post processing Techniques of Associative Classification for Malware Detection," IEEE Trans. Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 40, pp. 298307, May 2010. ##[36] A. Sami, B. Yadegari, H. Rahimi, N. Peiravian, S. Hashemi, and A. Hamze, "Malware detection based on mining API calls," In Proceedings of ACM Symposium on Applied Computing (SAC 10), Switzerland, pp. 10201025, March 2010. ##[37] G. Tahan, L. Rokach, and Y. Shahar, "MalID: Automatic Malware Detection Using Common Segment Analysis and MetaFeatures," The J. of Machine Learning Research, Vol. 13, pp. 949979, 2012. ##[38] M. K. Shankarapani, S. Ramamoorthy, R. S. Movva, and S. Mukkamala, "Malware detection using assembly and API call sequences," J. in Computer Virology, Vol. 7, pp. 107119, 2010. ##[39] P. M. Comparetti, G. Salvaneschi, E. Kirda, C. Kolbitsch, C. Kruegel, and S. Zanero, "Identifying Dormant Functionality in Malware Programs," IEEE Symposium on Security and Privacy (S&P 2010), Berleley/Oakland. California. USA, pp. 6176, May 2010. ##[40] M. Christodorescu, S. Jha, and C. Kruegel, "Mining specifications of malicious behavior," Foundations of Software Engineering, pp. 110, 2007. ##[41] L. Bai, J. Pang, Y. Zhang, W. Fu, and J. Zhu, "Detecting malicious behavior using critical API calling graph matching," Proceedings of the 1st Int. Conf. on Information Science and Engineering, Nanjing, pp. 17161719, 2009. ##[42] H. Guo, J. Pang, Y. Zhang, F. Yue, and R. Zhao, "HERO: A novel malware detection framework based on binary translation," Proceedings of the IEEE Int. Conf. on Intelligent Computing and Intelligent Systems, Xiamen, pp. 411415, 2010. ##[43] Y. Park, D. Reeves, V. Mulukutla, and B. Sundaravel, "Fast malware classification by automated behavioral graph matching," Proceedings of the 6th Annual Workshop on Cyber Security and Information Intelligence Research, USA, 2010. ##[44] F. Karbalaee, A. Sami, and M. Ahmadi, "Semantic Malware Detection by Deploying Graph Mining," Int. J. of Computer Science Issues (IJCSI 2012), Vol. 9, pp. 373379, 2012. ##[45] O. Kostakis, J. Kinable, H. Mahmoudi, and K. Mustonen, "Improved call graph comparison using simulated annealing," Proceedings of the 2011 ACM Symposium on Applied Computing, USA, pp. 15161523, 2011. ##[46] Y. Park, and D. Reeves, "Deriving common malware behavior through graph clustering", Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, USA, pp. 497502, 2011. ##[47] M. Ahmadi, A. Sami, H. Rahimi, and B. Yadegari, "Iterative System Call Patterns Blow the Malware Cover," IT Security for The Next Generation, Asia Pacific & MEA Cup 2011, Malaysia, March 2011. ##[48] G. Wagener, R. State, and A. Dulaunoy, "Malware behavior analysis," J. in Computer Virology, Vol. 4, pp. 279287, 2008. ##[49] U. Bayer, P. M. Comparetti, C. Hlauschek, C. Kruegel, and E. Kirda, "Scalable, Behavior Based Malware Clustering," Proceedings of the 16th Annual Network and Distributed System Security Symposium (NDSS'09), San Diego, February 2009. ##[50] U. Bayer, E. Kirda, and C. Kruegel, "Improving the Efficiency of Dynamic Malware Analysis," In Proceedings of the 2010 ACM Symposium on Applied Computing (SAC '10), NY, USA, pp. 18711878, 2010. ##[51] J. Jang, D. Brumley, and S. Venkataraman, "Bit Shred: Feature Hashing Malware for Scalable Triage and Semantic Analysis," Proceedings of the 18th ACM conf. on Computer and Communications Security, ACM, pp. 309320, 2011. ##[52] J. Hegedus, Y. Miche, A. Ilin, and A. Lendasse, "Methodology for Behavioralbased Malware Analysis and Detection Using Random Projections and KNearest Neighbors Classifiers," In Proceedings of the Seventh Int. Conf. on Computational Intelligence and Security, Sanya, Hainan, China, pp. 10161023, 2011. ##[53] J. Potier, "WinAPIOverride32," 2013. [Online]. Available electronically at http://jacquelin.potier.free.fr/winapioverride32/. ##[54] M. Fredrikson, S. Jha, M. Christodorescu, "Synthesizing NearOptimal Malware Specification from Suspicious Behaviors," Proceeding 31st IEEE Symposium on Security and Privacy (S&P 2010), pp. 4560, 2010. ##[55] R. Kohavi, "A study of crossvalidation and bootstrap for accuracy estimation and model selection," In Proceedings of the Fourteenth Int. Joint Conf. on Artificial Intelligence, pp. 11371143, 1995. ##[56] L. Breiman, "Random Forests," Kluwer Academic Publishers. Manufactured in The Netherlands. 2001. ##[57] T. Langerud, "PowerScan: A Framework for Dynamic Analysis and AntiVirus Based Identification of Malware," Master thesis, Norwegian University of Science and Technology Department of Telematics, Norway, 2008. ##[58] C. G. Weng, and J. Poon. "A New Evaluation Measure for Imbalanced Datasets," In Seventh Australasian Data Mining Conf. (AusDM 2008), pp. 2732, 2008. ##[59] A. Fog, "Function calling conventions," In Calling conventions for different C++ compilers and operating systems, Copenhagen, Denmark, 2012. ##[60] J. Potier, "Where is located the return value?," [Online]. Available electronically at http://jacquelin.potier.free.fr/winapioverride32/doc/faq.ht m#returnvalue, 2011. ##[61] Intel Corporation, "Intel Itanium Processor specific Application Binary Interface (ABI) Intel," 2001. ##]
Robust multiplicative video watermarking using statistical modeling
2
2
The present paper is intended to present a robust multiplicative video watermarking scheme. In this regard, the video signal is segmented into 3D blocks like cubes, and then, the 3D wavelet transform is applied to each block. The low frequency components of the wavelet coefficients are then used for data embedding to make the process robust against both malicious and unintentional attacks. The hidden message is inserted through multiplying/dividing these coefficients by a constant parameter which controls the power of the watermark. The watermark extraction relies on a maximum likelihoodbased procedure, observing the distribution of the watermarked coefficients. The performance of the proposed scheme has been verified via simulations and found to be superior to some of the wellknown existing video watermarking methods.
1

83
95


A.
Diyanat
Iran
a.diyanat@ut.ac.ir


M. A.
Akhaee
Iran
akhaee@ut.ac.ir


Sh.
Ghaemmaghami
Iran
ghaemmagh@sharif.edu
Multiplicative Video Watermarking
Maximum Likelihood Decoding
3D Wavelet Transform
[[1] G. Döerr, “A Guide Tour of Video Watermarking,” Signal Processing: Image Communication, vol. 18, pp. 263282, Apr. 2003. ##[2] Y. Chen and H. Huang, “A New ShotBased Video Watermarking,” in Computer Communication Control and Automation (3CA), International Symposium on, vol. 2, pp. 5358, 2010. ##[3] M. Belhaj, M. Mitrea, F. Preteux, and S. Duta, “MPEG4 AVC robust video watermarking based on QIM and perceptual masking,” in Communications (COMM), 8th International Conference on, pp. 477480,2010. ##[4] D. Xu, R. Wang, and J. Wang, “Low complexity video watermarking algorithm by exploiting CAVLC in H. 264/AVC,” in Wireless Communications, Networking and Information Security (WCNIS), IEEE International Conference on, pp. 411415, 2010. ##[5] L. Zhang, Y. Zhu, and L. L.M. Po, “A novel watermarking scheme with compensation in bit stream domain for H.264/AVC,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 17581761, Mar. 2010. ##[6] G. Langelaar, R. Lagendijk, and J. Biemond, “RealTime Labeling of MPEG2 Compressed Video,” Journal of Visual Communication and Image Representation, vol. 9, no. 4, pp. 256270, 1998. ##[7] S. K. Bavipati and X. Su, “Secure Compressed Domain Watermarking for H.264 Video,” Seventh International Conference on Information Technology: New Generations, pp. 387391, Apr. 2010. ##[8] S. N. Biswas, S. Nahar, S. R. Das, E. M. Petriu, M. H. Assaf, and V. Groza, “MPEG2 digital video watermarking technique,” in IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 225229, IEEE, May 2012. ##[9] R. Lancini, F. Mapelli, and S. Tubaro, “A Robust Video Watermarking Technique In the Spatial Domain,” in Video/Image Processing and Multimedia Communications 4th EURASIPIEEE Region 8 International Symposium on VIPromCom, no. June, pp. 251256, 2002 ##[10] P. Chan and M. Lyu, “A DWTbased digital video watermarking scheme with error correcting code,” in Proceedings of Fifth International Conference on Information and Communications Security, pp. 202213, Springer, 2003. ##[11] F. Deguillaume, G. Csurka, J. O’Ruanaidh, and T. Pun, “Robust 3D DFT Video Watermarking,” in Proceedings of IS & T/SPIE Electronic Imaging, vol. 3657, pp. 113124, 1999. ##[12] J. Zhang, J. Li, and L. Zhang, “Video watermark technique in motion vector,” in Computer Graphics and Image Processing, Proceedings of XIV Brazilian Symposium on, pp. 179182, 2001. ##[13] C. Kung, J. Jeng, Y. Lee, H. Hsiao, and W. Cheng, “Video watermarking using motion vector,” in Proc. of 16th IPPR Conference on computer vision, graphics and image processing, no. Cvgip, pp. 547551, 2003. ##[14] B. Barakli and C. Vural, “A new reversible video watermarking methodbased on motion compensated interpolation,” in 20th Signal Processing and Communications Applications Conference (SIU), pp. 14, IEEE, Apr. 2012. ##[15] B. Mobasseri, “Direct Sequence Watermarking of Digital Video Using MFrames,” in Proceedings International Conference on Image Processing (ICIP98), vol. 2, pp. 399403, 1998. ##[16] A. M. Kothari and V. V. Dwivedi, “Transform Domain Video Watermarking: Design, Implementation and Performance Analysis,” in International Conference on Communication Systems and Network Technologies, pp. 133137, IEEE, May 2012. ##[17] S. a. M. AlTaweel and P. Sumari, “Robust Video Watermarking Based On 3DDWT Domain,” in TENCON , IEEE Region 10 Conference, pp. 16, Nov. 2010. ##[18] P. Campisi, “Video watermarking in the 3DDWT domain using perceptual masking,” in IEEE International Conference on Image Processing(ICIP), pp. 9971000, 2005. ##[19] R. Reyes, C. Cruz, M. NakanoMiyatake, and H. PerezMeana, “Digital Video Watermarking in DWT Domain Using Chaotic Mixtures,” Latin America Transactions, IEEE (Revista IEEE America Latina), vol. 8, no. 3, pp. 304310, 2010. ##[20] R. O. Preda, “Robust waveletbased video watermarking scheme for copyright protection using the human visual system,” Journal of Electronic Imaging, vol. 20, p. 013022, Jan. 2011. ##[21] M. Swanson and A. Tewfik, “Multi resolution SceneBased Video Watermarking Using Perceptual Models,” IEEE Journal on Selected Areasin Communications, vol. 16, pp. 540550, May 2002. ##[22] J. Sun, N. Yang, J. Liu, X. Yang, X. Li, and L. Zhang, “Video watermarking scheme based on spatial relationship of DCT coefficients,” in Intelligent Control and Automation (WCICA), 8th World Congress on, pp. 5659, 2010. ##[23] E. E. Abdallah, A. Ben Hamza, and P. Bhattacharya, “Video watermarking using wavelet transform and tensor algebra,” Signal, Image and Video Processing, vol. 4, pp. 233245, Apr. 2009. ##[24] X. Guojuan and W. Rangding, “A Blind Video Watermarking Algorithm Resisting to Rotation Attack,” International Conference on Computer and Communications Security, pp. 111114, Dec. 2009. ##[25] C.X. Wang, X. Nie, X. Wan, W. B. Wan, and F. Chao, “A Blind Video Watermarking Scheme Based on DWT,” Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, vol. 1, pp. 434437, Sept. 2009. ##[26] R. C. Motwani, M. C. Motwani, B. D. Bryant, F. C. Harris Jr., and A. S. Agarwal, “Watermark Embedded Optimization for 3D Mesh Objects Using Classification Based Approach,” International Conference on Signal Acquisition and Processing, pp. 125129, Feb. 2010. ##[27] D. Pu, Y. Lu, and J. Dai, “Video watermarking approach based on temporal difference and discrete wavelet transform,” in Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference on, vol. 1, pp. 346350, 2010. ##[28] M. A. Akhaee, S. M. E. Sahraeian, B. Sankur, and F. Marvasti, “Robust ScalingBased Image Watermarking Using Maximum Likelihood Decoder With Optimum Strength Factor,” IEEE Transactions on Multimedia, vol. 11, pp. 822833, Aug. 2009. ##[29] I. I. Cox, J. Kilian, F. F. Leighton, and T. Shamoon, “Secure Spread Spectrum Watermarking For Multimedia,” Image Processing, IEEE Transactions on, vol. 6, no. 12, pp. 16731687, 2002. ##[30] M. Barni, F. Bartolini, A. D. Rosa, and A. Piva, “A new decoder for the optimum recovery of nonadditive watermarks,” IEEE transactions on image processing, vol. 10, pp. 75566, Jan. 2001. ##[31] J. Wang, G. Liu, Y. Dai, J. Sun, Z. Wang, and S. Lian, “Locally optimum detection for Barni’s multiplicative watermarking in DWT domain,” Signal Processing, vol. 88, pp. 117130, Jan. 2008. ##[32] S. Vassilios and P. Ioannis, “Optimal Detector for Multiplicative Watermarks Embedded in the DFT Domain of NonWhite Signals,” EURASIP Journal on Advances in Signal Processing, vol. 1900, no. 16, pp. 25222532, 2004. ##[33] T. 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Image encryption based on chaotic tent map in time and frequency domains
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The present paper is aimed at introducing a new algorithm for image encryption using chaotic tent maps and the desired key image. This algorithm consists of two parts, the first of which works in the frequency domain and the second, in the time domain. In the frequency domain, a desired key image is used, and a random number is generated, using the chaotic tent map, in order to change the phase of the plain image. This change in the frequency domain causes changes in the pixels value and shuffles the pixels location in the time domain. Finally, in the time domain, a pseudo random image is produced using a chaotic tent map, to be combined to the image generated through the first step, and thus the final encrypted image is created. A computer simulation is also utilized to evaluate the proposed algorithm and to compare its results to images encrypted by other methods. The criteria for these comparisons are chisquare test of histogram, correlation coefficients of pixels, NPCR (number of pixel change rate), UACI (unified average changing intensity), MSE (mean square error) and MAE (mean absolute error), key space, and sensitivity to initial condition. These comparisons reveal that the proposed chaotic image encryption method shows a higher performance, and is of more secure.
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E.
Hassani
Iran
e.hasani@srbiau.ac.ir


M.
Eshghi
Iran
meshghi@sbu.ac.ir
Image Encryption
Chaotic Tent Map
Key Image
Frequency Domain
Time Domain
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Persian Abstract
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