Visual Feature Based Image Forgery Detection

  • Authors

    • D. Vaishnavi
    • D. Mahalakshmi
    • Venkata Siva Rao Alapati
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20436
  • Image splicing forgery, local binary pattern, SVM, BPNN, combined k-NN.

  • Abstract

    In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positive rate are estimated and also comparative study is carried out by previous existing methods.

     

     

  • References

    1. [1] Shivani, S., Singh, D., and Agarwal, S. (2013) DCT Based Approach for Tampered Image Detection and Recovery Using Block Wise Fragile Watermarking Scheme. In Pattern Recognition and Image Analysis, pp 640–647, Springer.

      [2] Vaishnavi, D., and Subashini, T. (2015) Image Tamper Detection Based on Edge Image and Chaotic Arnold Map, Indian Journal of Science and Technology 8, 548–555.

      [3] He, H., Zhang, J., and Chen, F. (2008) A self-recovery fragile watermarking scheme for image authentication with superior localization, Science in China Series F: Information Sciences, SP Science in China Press 51, 1487–1507.

      [4] Rawat, S., and Raman, B. (2011) A chaotic system based fragile watermarking scheme for image tamper detection, AEU-International Journal of Electronics and Communications, Elsevier 65, 840–847.

      [5] Vaishnavi, D., and Subashini, T. (2015) Fragile Watermarking Scheme Based on Wavelet Edge Features, JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, KOREAN INST ELECTR ENG 901 KSTC, 635-4 YEOKSAM-DONG, GANGNAM-GU, SEOUL, 135-703, SOUTH KOREA 10, 2149–2154.

      [6] Lu, C.-S., and Liao, H.-Y. M. (2003) Structural digital signature for image authentication: an incidental distortion resistant scheme, Multimedia, IEEE Transactions on 5, 161–173.

      [7] Zhang, H.-B., Yang, C., and Quan, X.-M. (2004) Image authentication based on digital signature and semi-fragile watermarking, Journal of Computer Science and Technology, Springer 19, 752–759.

      [8] Wang, X., Xue, J., Zheng, Z., Liu, Z., and Li, N. (2012) Image forensic signature for content authenticity analysis, Journal of Visual Communication and Image Representation, Elsevier 23, 782–797.

      [9] Farid, H. (2009) Image forgery detection–A survey, Citeseer.

      [10] Fridrich, A. J., Soukal, B. D., and Luk, A. J. (2003) Detection of copy-move forgery in digital images. In in Proceedings of Digital Forensic Research Workshop, Citeseer.

      [11] Wu, Y., Deng, Y., Duan, H., and Zhou, L. (2014) Dual tree complex wavelet transform approach to copy-rotate-move forgery detection, Science China Information Sciences, Science China Press 57, 1–12.

      [12] Muhammad, G., Hussain, M., and Bebis, G. (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform, Digital Investigation, Elsevier 9, 49–57.

      [13] Pan, X., and Lyu, S. (2010) Region duplication detection using image feature matching, Information Forensics and Security, IEEE Transactions on, IEEE 5, 857–867.

      [14] Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., and Serra, G. (2011) A sift-based forensic method for copy–move attack detection and transformation recovery, Information Forensics and Security, IEEE Transactions on, IEEE 6, 1099–1110.

      [15] Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Del Tongo, L., and Serra, G. (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage, Signal Processing: Image Communication, Elsevier 28, 659–669.

      [16] Kakar, P., and Sudha, N. (2012) Exposing postprocessed copy paste forgeries through transform invariant features, Information Forensics and Security, IEEE Transactions on, IEEE 7, 1018–1028.

      [17] Mishra, P., Mishra, N., Sharma, S., and Patel, R. (2013) Region Duplication Forgery Detection Technique Based on SURF and HAC, The Scientific World Journal, Hindawi Publishing Corporation 2013.

      [18] Zhu, Y., Shen, X., and Chen, H. (2015) Copy-move forgery detection based on scaled ORB, Multimedia Tools and Applications, Springer 1–13.

      [19] Guo, J.-M., Liu, Y.-F., and Wu, Z.-J. (2013) Duplication forgery detection using improved DAISY descriptor, Expert Systems with Applications, Elsevier 40, 707–714.

      [20] Jiming ZHENG, P. L. (2014) Detection of Copy-move Forgery in Digital Image using DAISY Descriptor, Journal of Computational Information Systems, Binary Information Press 10, 9369–9377.

      [21] Anand, V., Hashmi, M. F., and Keskar, A. G. (2014) A copy move forgery detection to overcome sustained attacks using dyadic wavelet transform and sift methods. In Intelligent Information and Database Systems, pp 530–542, Springer.

      [22] Hashmi, M. F., Hambarde, A. R., and Keskar, A. G. (2013) Copy move forgery detection using DWT and SIFT features. In Intelligent Systems Design and Applications (ISDA), 2013 13th International Conference on, pp 188–193, IEEE.

      [23] Vaishnavi, D., and Subashini, T. (2015) A passive technique for image forgery detection using contrast context histogram features, International Journal of Electronic Security and Digital Forensics, Inderscience Publishers (IEL) 7, 278–289.

      [24] Alfraih, A. S., Briffa, J. A., and Wesemeyer, S. (2014) Cloning localization based on feature extraction and k-means clustering. In Digital-Forensics and Watermarking, pp 410–419, Springer.

      [25] Fischler, M. A., and Bolles, R. C. (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, ACM 24, 381–395.

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  • How to Cite

    Vaishnavi, D., Mahalakshmi, D., & Siva Rao Alapati, V. (2018). Visual Feature Based Image Forgery Detection. International Journal of Engineering & Technology, 7(4.6), 86-90. https://doi.org/10.14419/ijet.v7i4.6.20436

    Received date: 2018-09-29

    Accepted date: 2018-09-29

    Published date: 2018-09-25