An Integrated Technique for Image Forgery Detection using Block and Keypoint Based Feature Techniques

  • Authors

    • Leela Apurupa
    • J D.Dorathi Jayaseeli
    • D Malathi
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16168
  • copy-move, forgery detection, adaptive over-segmentation, feature point matching, neighboring blocks, super pixels, feature points

  • Abstract

    The invention of the net has introduced the unthinkable growth and developments within the illustrious analysis fields like drugs, satellite imaging, image process, security, biometrics, and genetic science. The algorithms enforced within the twenty first century has created the human life more leisurely and secure, however the protection to the first documents belongs to the genuine person is remained as involved within the digital image process domain. a replacement study is planned during this analysis paper to discover. The key plan in the deliberate take a look at and therefore the detection of the suspected regions are detected via the adaptive non-overlapping and abnormal blocks and this method is allotted exploitation the adaptive over-segmentation algorithmic rule. The extraction of the feature points is performed by playacting the matching between every block and its options. The feature points are step by step replaced by exploitation the super pixels within the planned Forgery Region Extraction algorithm then merge the neighboring obstructs that have comparative local shading decisions into the element squares to encourage the brought together districts; at last, it applies the morphological activity to the bound together areas to ask the recognized falsification districts The planned forgery detection algorithmic rule achieves far better detection results even below numerous difficult conditions the sooner strategies all told aspects. We have analyzed the results obtained by the each SIFT and SURF and it is well-tried that the planned technique SURF is giving more satisfactory results by both subjective and objective analysis.

     

     

  • References

    1. [1] Yang and C.-L. Huang, "Copymove Forgery Detection in Digital Image," Advances in Multimedia Information Processing-PCM 2009, Ed: Springer, Pp. 816-825, 2009.

      [2] Mahdian And S. Saic, "Blind Methods For Detecting Image Fakery," IEEE Aerospace And Electronic Systems Magazine, Vol. 25, Pp. 18-24, 2010.

      [3] Shivakumar and L. D. S. Santhosh Baboo, "Detecting Copy-Move Forgery in Digital Images: A Survey and Analysis of Current Methods," Global Journal of Computer Science And Technology, Vol. 10, 2010.

      [4] K. N. Qureshi and A. H. Abdullah, "A Survey on Intelligent Transportation Systems," Middle East Journal of Scientific Research, Vol. 15, 2013.

      [5] W. Lu, W. Sun, J.-W. Huang, And H.-T. Lu, "Digital Image Forensics Using Statistical Features and Neural Network Classifier," In Machine Learning and Cybernetics, 2008 International Conference on, Pp. 2831-2834, 2008.

      [6] S. Bayram, B. Sankur, N. Memon, and Ä°. AvcibaÅŸ, "Image Manipulation Detection," Journal of Electronic Imaging, Vol. 15, pp. 041102-041102-17, 2006.

      [7] Popescu and H. Farid, "Exposing Digital Forgeries By Detecting Traces of Resampling," Signal Processing, IEEE Transactions On, Vol. 53, pp. 758-767, 2005.

      [8] E. Dirik, S. Bayram, H. T. Sencar, and N. Memon, "New Features To Identify Computer Generated Images," In Image Processing, 2007. ICIP 2007. IEEE International Conference On, pp. Iv-433-Iv-436, 2007.

      [9] M. Kharrazi, H. T. Sencar, and N. Memon, "Blind Source Camera Identification," In Image Processing, 2004. ICIP'04. 2004 International Conference On, pp. 709-712, 2004.

      [10] M.-J. Tsai and G.-H. Wu, "Using Image Features To Identify Camera Sources," In Acoustics, Speech And Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference On, 2006.

      [11] M.-J. Tsai and C.-S. Wang, "Adaptive Feature Selection for Digital Camera Source Identification," In Circuits and Systems, 2008. Iscas 2008. IEEE International Symposium On, pp. 412-415, 2008.

      [12] Y. Sutcu, S. Bayram, H. T. Sencar, and N. Memon, "Improvements on Sensor Noise Based Source Camera Identification," In Multimedia And Expo, 2007 IEEE International Conference On, pp. 24-27, 2007.

      [13] R. Bausvs and A. Kriukovas, "Digital Signature Approach for Image Authentication," Electronics & Electrical Engineering, 2008.

      [14] T. Chen, J. Wang, and Y. Zhou, "Combined Digital Signature and Digital Watermark Scheme for Image Authentication," In Info-Tech and Info-Net, 2001. Proceedings. ICII 2001-Beijing. 2001 International Conferences On, pp. 78-82, 2001.

      [15] X. Zhou, X. Duan, and D. Wang, "A Semifragile Watermark Scheme for Image Authentication," In Multimedia Modelling Conference, 2004. Proceedings. 10th International, pp. 374-377, 2004.

      [16] M. Sridevi, C. Mala, and S. Sanyam, "Comparative Study of Image Forgery and Copy-Move Techniques," In Advances In Computer Science, Engineering & Applications, Ed: Springer, Pp. 715-723, 2012.

      [17] S. Rawat and B. Raman, "A Chaotic System Based Fragile Watermarking Scheme for Image Tamper Detection," AEU-International Journal of Electronics and Communications, Vol. 65, pp. 840-847, 2011.

      [18] M. Kirchner and R. Bohme, "Hiding Traces of Resampling in Digital Images," Information Forensics And Security, IEEE Transactions on, Vol. 3, pp. 582-592, 2008.

      [19] H. Shah, P. Shinde, and J. Kukreja, "Retouching Detection And Steganalysis," IJEIR, Vol. 2, pp. 487- 490, 2013.

      [20] R. Granty, T. Aditya, and S. Madhu, "Survey on Passive Methods of Image Tampering Detection," In Communication And Computational Intelligence (INCOCCI), 2010 International Conference On, pp. 431-436, 2010.

      [21] M. Sridevi, C. Mala, and S. Sandeep, "Copy–Move Image Forgery Detection in a Parallel Environment," 2012.

      [22] W. Luo, J. Huang, and G. Qiu, "Robust Detection of Region-Duplication Forgery in Digital Image," In Pattern Recognition, 2006. ICPR 2006. 18th International Conference On, pp. 746-749, 2006.

      [23] J. Wang, G. Liu, H. Li, Y. Dai, and Z. Wang, "Detection of Image Region Duplication Forgery Using Model With Circle Block," In Multimedia Information Networking and Security, 2009. Mines'09. International Conference on Mines, pp. 25-29, 2009.

      [24] N. D. Wandji, S. Xingming, and M. F. Kue, "Detection of Copy-Move Forgery in Digital Images Based On DCT," International Journal of Computer Science Issues (IJCSI), Vol. 10, 2013.

      [25] S. Bayram, H. T. Sencar, and N. Memon, "An Efficient and Robust Method for Detecting Copy-Move Forgery," In Acoustics, Speech And Signal Processing, 2009. ICASSP 2009. IEEE International Conference, pp. 1053-1056, 2009.

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  • How to Cite

    Apurupa, L., D.Dorathi Jayaseeli, J., & Malathi, D. (2018). An Integrated Technique for Image Forgery Detection using Block and Keypoint Based Feature Techniques. International Journal of Engineering & Technology, 7(3.12), 505-511. https://doi.org/10.14419/ijet.v7i3.12.16168

    Received date: 2018-07-24

    Accepted date: 2018-07-24

    Published date: 2018-07-20