iosr-JCE   Volume-14 ~ Issue-4

Abstract: Privacy Preserving Data Mining(PPDM) is a rising field of research in Data Mining and various approaches are being introduced by the researchers. One of the approaches is a sanitization process, that transforms the source database into a modified one by removing selective items so that the counterparts or adversaries cannot extract the hidden patterns from. This study address this concept and proposes a revised Item-based Maxcover Algorithm(IMA) which is aimed at less information loss in the large databases with minimal removal of items.

Keywords: Privacy Preserving Data Mining, Restrictive Patterns, Sensitive Transactions, Maxcover, Sanitized database.

[1] Verykios,V.S, Bertino.E, Fovino.I.N, ProvenzaL.P, Saygin.Y and Theodoridis.Y, "State-of-the-art in Privacy Preservation Data Mining", New York,ACM SIGMOD Record, vol.33, no.2, pp.50-57,2004.

[2] Atallah.M, Bertino.E, Elmagarmid.A, Ibrahim.M and Verykios.V.S, "Disclosure Limitation of Sensitive Rules", In Proc. of IEEE Knowledge and Data Engineering Workshop, pages 45–52, Chicago, Illinois, November 1999.

[3] Clifton.C and Marks.D, " Security and Privacy Implications of Data Mining", In Workshop on Data Mining and Knowledge Discovery, pages 15–19, Montreal, Canada, February 1996.

[4] Dasseni.E, Verykios.V.S, Elmagarmid.A.K and Bertino.E, " Hiding Association Rules by Using Confidence and Support", In Proc. of the 4th Information Hiding Workshop, pages 369– 383, Pittsburg, PA, April 2001.

[5] Saygin.Y, Verykios.V.S, and Clifton.C, "Using Unknowns to Prevent Discovery of Association Rules", SIGMOD Record, 30(4):45–54, December 2001.

[6] Oliveira.S.R.M and Zaiane.O.R, "Privacy preserving Frequent Itemset Mining", in the Proc. of the IEEE ICDM Workshop on Privacy, Security, and Data Mining, Pages 43-54, Maebashi City, Japan, December 2002.

[7] Oliveira.S.R.M and Zaiane.O.R, "An Efficient One-Scan Sanitization for Improving the Balance between Privacy and Knowledge Discovery", Technical Report TR 03-15, June 2003.

[8] Cynthia Selvi P, Mohamed Shanavas A.R, "An Effective Heuristic Approach for Hiding Sensitive Patterns in Databases", IOSR-Journal on Computer Engineering, Volume 5, Issue 1(Sep-Oct, 2012), PP 06-11, DOI. 10.9790/0661-0510611.

[9] Agrawal R, Imielinski T, and Swami.A, "Mining association rules between sets of items in large databases", Proceedings of 1993 ACM SIGMOD international conference on management of data, Washington, DC; 1993. p. 207-16.
[10] http://fimi.cs.helsinki.fi/data/

Abstract: Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human's ability to analyze. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial database. The development of clustering algorithms has received a lot of attention in the last few years and new clustering algorithms are proposed. DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data containing noise and outliers. This paper shows the results of analyzing the properties of density based clustering characteristics of three clustering algorithms namely DBSCAN, k-means and SOM using synthetic two dimensional spatial data sets.

Keywords: Clustering, DBSCAN, K-Means, SOM, SOFM

[1] Kaufman L. and Rousseeuw P. J (1990), "Finding Groups in Data: An Introduction to Cluster Analysis", John Wiley & Sons.

[2] Ankerst M., Markus M. B., Kriegel H., Sander J(1999), "OPTICS: Ordering Points To Identify the Clustering Structure", Proc.ACM SIGMOD‟99 Int. Conf. On Management of Data, Philadelphia, PA, pp.49-60.

[3] Guha S, Rastogi R, Shim K (1998), "CURE: An efficient clustering algorithm for large databases", In: SIGMOD Conference, pp.73~84.

[4] Ester M., Kriegel H., Sander J., Xiaowei Xu (1996), "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", KDD‟96, Portland, OR, pp.226-231.

[5] Wang W., Yang J., Muntz R(1997), "STING: A statistical information grid approach to spatial data mining", In: Proc. of the 23rd VLDB Conf. Athens, pp.186~195.

[6] Raymond T. Ng and Jiawei Han (2002), "CLARANS: A Method for Clustering Objects for Spatial Data Mining", IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 5.

[7] Rakesh A., Johanners G., Dimitrios G., Prabhakar R(1999), "Automatic subspace clustering of high dimensional data for data mining applications", In: Proc. of the ACM SIGMOD, pp.94~105. pp.975-982.

Abstract: In medical science, the importance of the Electrocardiography is remarkable since heart diseases constitute one of the major causes of mortality in the world. Electrocardiogram (ECG) is the only way for doctors to see the cardiac actions of a particular person. It provides a graphic depiction of the electrical forces generated by the heart and then by analysing this graph doctors can tell about any abnormality present in heart. In the paper we focus on the QRS complex detection in electrocardiogram and the idea of further recognition of anomalies in QRS complexes based on some dimensional features of ECG is described. As medical information system is widely used and growing medical databases requires efficient classification method for efficient computer assisted analysis of ECG.

[1] Urszula Markowska-Kaczmar et al, "Mining of an Electrocardiogram" in XXI Autumn Meeting of Polish Information Processing Society Conference Proceedings pp. 169–175, 2005.

[2] Chia-Hung Lin et al, "Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavelet Network", in proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September pp1-4, 2005.

[3] P. Sasikala, Dr. R.S.D. Wahidabanu, "Robust R Peak and QRS detection in Electrocardiogram Using Wavelet Transform", in International Journal of Advanced Computer Science and Applications, Vol. 1, No.6, December 2010.

[4] V.S. Chouhan et al, "Delineation of QRS-complex, P and T-wave in 12-lead ECG", in International Journal of Computer Science and Network Security, VOL.8 No.4, April 2008

[5] A. Dallali et al, " Integration of HRV, WT and Neural Networks for ECG Arrhythmias Classification", in ARPN Journal of Engineering and Applied Sciences, VOL. 6, NO. 5, May 2011.

[6] S. S. Mehta et al, "Comparative Study of QRS Detection in Single Lead and 12-Lead ECG Based on Entropy And Combined Entropy Criteria Using Support Vector Machine", in Journal of Theoretical and Applied Information Technology, 2007.

[7] Victor Dan Moga et al, "Wavelets As Methods For ECG Signal Processing", University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 1, Timisoara Romania

[8] C. Saritha, V. Sukanya, Y. Narasimha Murthy, " ECG Signal Analysis Using Wavelet Transforms", Anantapur, Andhrapradesh, India.

[9] K.V.L.Narayana et al, "Noise removal using adaptive noise cancelling, analysis of ECG using MATLAB", in International Journal of Engineering Science and Technology, Vol. 3 No. 4, Apr 2011.

[10] Jiapu Pan, Willis J. Tompkins, "A Real Time QRS Detection Algorithm", in IEEE Transactions on Biomedical Engineering, Vol. 3 No. 3, March 1983

Abstract: The visual extent of an object reaches beyond the object itself. It is reflected in image retrieval techniques which combine statistics from the whole image in order to identify the image within. Nevertheless, it is still unclear to what degree and how this visual extent of an object affects the classification performance. Here we analyze the visual extent of an object on the Pascal VOC dataset using bag of words implementation with SIFT Descriptors. Our analysis is performed from two angles: (a) Not knowing the object location, we determine where in the image the support for object classification resides (normal situation) and (b) Assuming that the object location is known, we evaluate the relative potential of the object and its surround, and of the object border and object interior (ideal situation).

Key words: Computer vision, Content based image retrieval, Context, Extent of an Object, Visual extent

[1]. Agarwal, S., Awan, A., & Roth, D. (2004). Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11), 1475–1490.
[2]. Bar, M. (2004). Visual objects in context. Nature Reviews. Neuroscience, 5, 617–629.
[3]. Biederman, I. (1981). On the semantics of a glance at a scene. In Perceptual organization (pp. 213–263). Hillsdale: Lawrence Erlbaum.
[4]. Bishop, C. M. (2006). Pattern recognition and machine intelligence. Berlin: Springer.
[5]. Blaschko, M. B., & Lampert, C. H. (2009). Object localization with global and local context kernels. In British machine vision conference.
[6]. Burl, M. C., Weber, M., & Perona, P. (1998). A probabilistic approach to object recognition using local photometry and global geometry. In European conference on computer vision.
[7]. Carbonetto, P., de Freitas, N., & Barnard, K. (2004). A statistical model for general contextual object recognition. In European conference on computer vision. Berlin: Springer.
[8]. Csurka, G., Dance, C. R., Fan, L.,Willamowski, J., & Bray, C. (2004). Visual categorization with bags of keypoints. In ECCV international workshop on statistical learning in computer vision, Prague.
[9]. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE conference on computer vision and pattern recognition.
[10]. Divvala, S. K., Hoiem, D., Hays, J. H., Efros, A. A., & Herbert, M. (2009). An empirical study of context in object detection. In IEEE conference on computer vision and pattern recognition.

Abstract: In a Wireless sensor network, specifically data aggregation reduces the amount of communication and energy utilization. Newly, the research centre has proposed a strong aggregation framework called synopsis diffusion which combines multipath routing schemes with duplicate-insensitive algorithms to perfectly compute aggregates (e.g., predicate Count, Sum) unkindness of message losing results from node and communication failures. But this aggregation framework does not solve the problems which are appearing at base station side. These problems may occur due to the irrespective of the network size, the per node communication over-head. In this paper, we make the synopsis diffusion approach secure against attacks in which compromised nodes put in false sub aggregate values. In particular, we present a novel lightweight verification algorithm by which the base station can determine if the computed aggregate (predicate Count or Sum) includes any false input.

Keywords: Sensor Networks, Aggregation, Security, Base Station, Randomized Multipath Routing.

[1] S. Nath, P. B. Gibbons, S. Seshan, and Z. Anderson, "Synopsis diffusionfor robust aggregation in sensor networks," in Proc. 2nd Int. Conf.Embedded Networked Sensor Systems (SenSys), 2004.

[2] D. Wagner, "Resilient aggregation in sensor networks," in Proc. ACMWorkshop Security of Sensor and Adhoc Networks (SASN), 2004.

[3] L. Hu and D. Evans, "Secure aggregation for wireless networks," inProc. Workshop Security and Assurance in Ad hoc Networks, 2003.

[4] T. Claveirole, M.D. de Amorim, M. Abdalla, and Y. Viniotis,"Securing Wireless Sensor Networks Against Aggregator Compromises,"IEEE Comm. Magazine, vol. 46, no. 4, Apr.2008.

[5] S.Roy, M.Conti, S.Setia, S.Jajodia, "Secure Data Aggregation in Wireless Sensor Network", IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, June 2012.

[6] T.Shu, M.Krunz, S.Liu, "Secure Data Collection in Wireless Sensor Networks Using Randomized Dispersive Routes" IEEE Transactions on Mobile Computing, vol. 9, no. 7, July 2010.

[7] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A Survey on Sensor Networks," IEEE Comm. Magazine, vol. 40, no. 8, Aug. 2002.

[8] M. Burmester and T.V. Le, "Secure Multipath Communication in Mobile Ad Hoc Networks," Proc. Int'l Conf. Information Technology: Coding and Computing, 2004.

[9] D.B. Johnson, D.A. Maltz, and J. Broch, "DSR: The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks," Ad Hoc Networking, C.E. Perkins, ed., Addison-Wesley, 2001.

[10] S.J. Lee and M. Gerla, "Split Multipath Routing with Maximally Disjoint Paths in Ad Hoc Networks," Proc. IEEE Int'l Conf. Comm. (ICC), 2001.. Tompkins, "A Real Time QRS Detection Algorithm", in IEEE Transactions on Biomedical Engineering, Vol. 3 No. 3, March 1983

Abstract: Managing data warehouses (DWs) is typically characterized by intensive data processing and protracted activities which usually degrade performance. Moreover, DWs are usually designed with the overall objective of making available the content to users, and for them to stay alive, all the management phases need to interact with external and heterogeneous sources. These obviously expose the system to wider security issues. In order to enhance performance, we present a scheme that utilizes the mobility characteristic of agents. The scheme is designed with well-defined communication interfaces within the agent structures in order to improve on the security, where communications within the DW must be done through communicators that require authentications.

Keywords: Agent, component, data warehouse, permission, security.

[1] V. Rodolfo, E. Fernández-Medina, M. Piattini, and J. Trujillo, "A UML 2.0/OCL Extension for Designing Secure Data Warehouses," Journal of Research and Practice in Information Technology, 38(1), pp.31-43, 2006.
[2] N. V. Blamah, G. Wajiga, and A. S. Ahmadu, "The Security of Mobile Agents," In Proceedings of the International Conference on NTMCS, Covenant University, Ota, Nigeria, 379-392, 2006.
[3] S. Chen, "Efficient Incremental View Maintenance for Data Warehousing," PhD Dissertation Submitted to the Faculty of The Worcester Polytechnic Institute, UK, 2005.
[4] Y. Qu, and W. Yang, "Improving the Security of the Distributed Enterprise Data Warehouse System," Intelligence and Security Informatics, IEEE International Conference, p.227, 2011.
[5] O. Idika, "Data Mining and Data Warehousing," A Publication of the Digital Bridge Institute, International Center for Advanced Communication Studies, Abuja, Nigeria, 2005.
[6] R. J. Santos, J. Bernardino, and M. Vieira, "Balancing Security and Performance for Enhancing Data Privacy in Data Warehouses," 2011 International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11, pp.242-249, 2011.
[7] W. Jansen, and T. Karygiannis, "Mobile Agents and Security," NIST Special Publication 800-19, 1999.
[8] A. Javed, and S. S. Rafique, "Data Warehouse Maintenance, Improving Data Warehouse Performance through Efficient Maintenance," Masters Thesis, Lulea University of Technology, Division of Information System Sciences, 2006.
[9] B. Liu, S. Chen, and E. A. Rundensteiner, "A Transactional Approach to Parallel Data Warehouse Maintenance," A Publication of the Worcester Polytechnic Institute, UK, 2002.
[10] S. Lujan-Mora, "Data Warehouse Design Using UML," PhD Thesis, Department of Software and Computing Systems, University of Alicante, 2005.

Abstract: Swarm Intelligence (SI) algorithms are heuristics for finding the optimal solutions of optimization problems. They are made up of groups of swarms that interact with one another in the search effort within their environment. A reflective SI algorithm is presented, where members of the swarm are able to reflect backward to reconsider historic actions in order to adjust their search behaviors and stick to better results, which make the algorithm to perform robustly.

Keywords: Swarm intelligence, heuristic, retrospective

[1] Parpinelli, R.S., and Lopes, H.S. (2011). New inspirations in swarm intelligence: a survey, International Journal of Bio-Inspired Computation, 3(1), pp.1–16.
[2] Bratman, M.E. (1990).What is Intention? In Cohen, P.R., Morgan, J.L., and Pollack, M.E., editors, Intentions in Communication, The MIT Press: Cambridge, MA.
[3] Brownlee, J. (2011).Clever Algorithms: Nature-Inspired Programming Recipes. 1st ed., Lulu. http://www.CleverAlgorithms.com
[4] Parsopoulos, K.E., and Vrahatis, M.N. (2010). Particle Swarm Optimization and Intelligence: Advances and Applications, Information Science Reference, Hershey, (NY).
[5] Confort, M and Meng, Y. (2008). Reinforcing Learning for Neural Networks using Swarm Intelligence, IEEE Swarm Intelligence Symposium, St. Louis MO, USA, Sept., 21-23, 2008.
[6] Davidson, H. (1992). Alfarabi, Avicenna, and Averroes, on Intellect, Oxford University Press, pp.6.
[7] Gupta, P., Sharma, K., and Singh, P. (2012). A Review of Object Tracking using Particle Swarm Optimizatio, VSRD, International Journal of Electrical Electronics & Comm. Engg. 2(7).
[8] Blamah, N. V., Adewumi, A. O. and Olusanya, M. O. (2013). A Secured Agent-Based Framework for Data Warehouse Management. Proceedings of IEEE International Conference on Industrial Technology (ICIT), pp1840-1845, Cape Town.
[9] Kennedy, J., and Eberhart, R. (1995). Particle Swarm Optimisation. In: Proceedings of the IEEE Conf. on Neural Networks, Perth.
[10] Wooldridge, M. (2009), An Introduction to MultiAgent Systems. 2nd ed., John Wiley & Sons Ltd, Chichester.

Abstract: An ad-hoc network is the cooperative engagement of a collection of Mobile Hosts without the required intervention of any centralized Access Point. A Mobile Ad hoc NETwork called MANET is a kind of wireless ad-hoc network that is a self configuring network of mobile routers. These mobile routers are connected by wireless links. In MANET there are various routing protocols available. DSR, AODV and DSDV are most popular. Our proposed works are related to examine routing protocol for mobile ad hoc networks -the Destination Sequenced Distance Vector (DSDV) and On Demand protocol that evaluates both protocols based on the packet delivery fraction and average delay while varying number of sources and pause time. In this Improved -DSDV approach we can overcome the problem of state routes, as well as improve the performance of regular DSDV. We compare the performance of our work with DSDV. In our improved DSDV routing protocol, nodes can cooperate together to obtain an objective opinion about another nodes trustworthiness.

Keywords:Wireless communications, Broken node, Security, nodes, AODV, DSDV, Packet Delivery Fraction, MANET.

[1] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

[2] M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989. Journal Paper:

[3] G. Eason, B. Noble, and I. N. Sneddon, "On certain integrals of Lipschitz-Hankel type involving products of Bessel functions," Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references)

[4] I. S. Jacobs and C. P. Bean, "Fine particles, thin films and exchange anisotropy," in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350. Proceeding Papers:

[5] K. Elissa, "Title of paper if known," unpublished.

[6] R. Nicole, "Title of paper with only first word capitalized," J. Name Stand. Abbrev., in press.

[7] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, "Electron spectroscopy studies on magneto-optical media and plastic substrate interface," IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].

Abstract: The most prominent applications of wireless sensors networks are coverage, Target detection and field surveillance. This paper investigates detection of a target traversing the region being monitored by some number of sensors using minimum exposure path. We derive minimum exposure path formula from Integral geometry. We represent the sensor field as connected grid of points. Then minimum exposure is calculated for different grids of points. We consider the random and deterministic placement of sensors. It illustrates that the target detection can be achieved by choosing the appropriate number of sensors and grid of points.

Keywords: coverage,deployment, exposure, wireless sensor networks

[1] I. F. Akyildiz et al., "A Survey on Sensor Networks", IEEE Comm.Mag., 40(8): Aug. 2002.pp .102–116
[2] E. Shih, S.-H. Cho, N. Ickes, R. Min, A. Sinha, A. Wang and A.Chandrakasan. "Physical layer driven protocol and algorithm design forenergy-efficient wireless sensor networks", in: ACM Int'l Conf. on Mobile Computing and Networking (MobiCom) (2001) pp. 272–287.
[3] W. Ye, J. Heidemann and D. Estrin, "An energy-efficient MAC protocol for wireless sensor networks", in: IEEE INFOCOM (2002) pp. 1567–1576.
[4] D. Ganesan, R. Govindan, S. Shenker and D. "Estrin, Highly resilient, energy efficient multipath routing in wireless sensor networks", ACM Mobile Comput. andCommun. Review 5(4) (2001) pp.11–25.
[5] W.R. Heinzelman, A. Chandrakasan and H. Balakrishnan, "Energyefficient communication protocols for wireless microsensor networks", in: Hawaii Int'l Conf. on Systems Science (HICSS) (2000).
[6] M. Cardei and D.-Z. Du, "Improving wireless sensor network lifetime through power aware organization," Wireless Netw., vol. 11, no. 3, (2005) pp.333–340,.
[7] J. O'Rourke, Computational geometry column 15, Int'l Journal of Computational Geometry and Applications 2(2) (1992),pp. 215–217.
[8] S. Meguerdichian, F. Koushanfar, M. Potkonjak and M.B. Srivastava, "Coverage problems in wireless ad-hoc sensor networks". in: IEEE INFOCOM (2001) pp. 1380–1387.
[9] S. Meguerdichian, F. Koushanfar, G. Qu and M. Potkonjak, Exposure in wireless ad-hoc sensor networks, in: ACM Int'l Conf. on MobileComputing and Networking (MobiCom) (2001) pp. 139–150
[10] S. Meguerdichian, S. Slijepcevic, V. Karayan and M. Potkonjak, Localized algorithms in wireless ad-hoc networks: location discovery and sensor exposure, in: ACM Int'l Symp. on Mobile Ad Hoc Networkingand Computing (MobiHOC) (2001) pp. 106–116.

Abstract: Speech recognition is a multileveled pattern recognition task, in which acoustical signals are examined and structured into a hierarchy of sub word units (e.g., phonemes), words, phrases, and sentences. Each level may provide additional temporal constraints, e.g., known word pronunciations or legal word sequences, which can compensate for errors or uncertainties at lower levels. This hierarchy of constraints can best be exploited by combining decisions probabilistically at all lower levels, and making discrete decisions only at the highest level.

Keywords: ASR (Automatic Speech Recognition)1; Dynamic Time Warping2; FET (Feature Extraction Technique)3.

[1] Sadaoki Furui, 50 years of Progress in speech and Speaker Recognition Research, ECTI Transactions on Computer and Information Technology,Vol.1. No.2, November 2005.

[2] K. H. Davis, R. Biddulph, and S. Balashek, Automatic recognition of spoken Digits, J. Acoust. Soc. Am., 24(6): 637-642, 1952.

[3] H. F. Olson and H. Belar, Phonetic Typewriter, J. Acoust. Soc. Am., 28(6): 1072-1081, 1956. [4] D. B. Fry, Theoritical Aspects of Mechanical speech Recognition, and P. Denes, The design and Operation of the Mechanical Speech Recognizer at Universtiy College London, J. British Inst. Radio Engr., 19: 4, 211 - 299, 1959.

[5] J.W. Forgie and C. D. Forgie, Results obtained from a vowel recognition computer program, J.A.S.A., 31(11), pp.1480-1489. 1959. [6] J. Suzuki and K. Nakata, Recognition of Japanese Vowels Preliminary to the Recognition of Speech, J. Radio Res. Lab 37(8):193-212, 1961.

[7] T. Sakai and S. Doshita, The phonetic typewriter, Information processing 1962, Proc .IFIP Congress, 1962.

[8] K. Nagata, Y. Kato, and S. Chiba, Spoken Digit Recognizer for Japanese Language, NEC Res. Develop, No. 6, 1963.

[9] T. B. Martin, A. L. Nelson, and H. J. Zadell, Speech Recognition & Feature Abstraction Techniques, Tech. Report AL-TDR-64-176, Air Force Avionics Lab, 1964.

[10] T. K. Vintsyuk, Speech Discrimination by Dynamic Programming, Kibernetika, 4(2):81-88, Jan.-Feb.1968.

Abstract: Day by day the increase in networked multimedia systems has created an urgent need for copyright enforcement technologies that can protect copyright ownership of multimedia contents. Digital Image Watermarking is one such technology that has been developed to protect digital images from illegal manipulations. Watermarking is used to To protect the ownership of content service provider is a crucial area of research. Robustness against geometric distortions is crucial issue in watermarking. In this paper, a new SVD-DWT semi-blind composite image watermarking algorithm that is robust against various attacks is presented. I used DWT and IDWT transform to obtain four different frequency images. Watermark is embedded in high- frequency band by SVD. The imperceptibility and robustness are the properties that are evaluated for the proposed scheme. Image is evaluated using peak-signal-to-noise ratio (PSNR) which is used to evaluate the difference between original image and the watermarked image.

Keywords: Copyright protection, Discrete wavelet transform (DWT), Multi frequency image, Singular value decomposition (SVD)

[1]. Dazhi Zhang, Wu,Sun and Huang , " A New Robust Watermarking Algorithm Based on DWT" Image and Signal processing 2009 ,CISP'09
[2]. Rowayda A Sadek , "Blind Synthesis Attack on SVD Based watermarking Technique" , Computational Intelligence for modeling Control :2008 pp 140-145.
[3]. E. Ganic and A. M. Eskicioglu, "Secure DWT-SVD Domain Image Watermarking: Embedding Data in All Frequencies," ACM Multimedia and Security Workshop 2004, Magdeburg, Germany, September 20-21, 2004.
[4]. Zheng, D., Liu, Y., Zhao, J., and El Saddik, A. "A survey of RST invariant image watermarking algorithms", ACM Computing Surveys, Volume 39, No. 2, Article 5, June 2007.
[5]. H. C. Andrews and C. L. Patterson, "Singular value decomposition (SVD) image coding," IEEE Transactions on Communication, vol. COM-24, pp. 425–432, Apr. 1976.
[6]. Zude Zhou, Bing Tang and Xinhua Liu, "A Block-SVD Based Image Watermarking Method", Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, 2006, Dalian, China
[7]. Chin-Chen Chang, Piyu Tsai, Chia-Chen Lin, "SVD-based digital image watermarking scheme", Pattern Recognition Letters, Volume 26, Issue 10, July 2005, pp. 1577-1586
[8]. Kapre and Joshi .,"Robust Image Watermarking based on Singular Value Decomposition and Discrete Wavelet Transform", Nanded ©2010 IEEE