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Reseach Article

Image Forgery Detection based on Local Descriptors and Block-Matching using Clustering Technique

by Shikha Dubey, Anshul Sarawagi, Manish Shrivastava
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 10
Year of Publication: 2016
Authors: Shikha Dubey, Anshul Sarawagi, Manish Shrivastava
10.5120/ijca2016909808

Shikha Dubey, Anshul Sarawagi, Manish Shrivastava . Image Forgery Detection based on Local Descriptors and Block-Matching using Clustering Technique. International Journal of Computer Applications. 141, 10 ( May 2016), 11-14. DOI=10.5120/ijca2016909808

@article{ 10.5120/ijca2016909808,
author = { Shikha Dubey, Anshul Sarawagi, Manish Shrivastava },
title = { Image Forgery Detection based on Local Descriptors and Block-Matching using Clustering Technique },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 141 },
number = { 10 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume141/number10/24818-2016909808/ },
doi = { 10.5120/ijca2016909808 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:07.749084+05:30
%A Shikha Dubey
%A Anshul Sarawagi
%A Manish Shrivastava
%T Image Forgery Detection based on Local Descriptors and Block-Matching using Clustering Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 10
%P 11-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In current decade, digital images are in use in a wide range of applications and for multiple purposes. They also play an important role in the storage and transfer of visual information, especially the secret ones. With this widespread usage of digital images, in addition to the increasing number of tools and software of digital images editing, it has become easy to manipulate and change the actual information of the image. In this detection technique used texture feature of image. For the texture extraction of image used wavelet transform function, these function is most promising texture analysis feature. For the selection of feature generation of pattern used clustering technique. Clustering technique is unsupervised learning technique process by iteration. The proposed methods are evaluated on a number of original and forged images. According to our experimental results the proposed methods are quite attractive. The forgery is done with just copy-move, copy-move with rotation, with scaling, and reflection. In this process, an image database that consists of original and forged images is also developed. The proposed method achieves 100% accuracy in just copy-move forgery (without any change in the size or characteristics of the object) forgery without post-processing and 98.43%, 86.58%, and 95.12% accuracies in copy-move forgery with rotation, scaling, and reflection, respectively.

References
  1. Abhishek Kashyap, Shiv Dutt Joshi, “Detection of Copy-Move Forgery Using Wavelet Decomposition” IEEE, 2013, Pp 396-400.
  2. Saba Mushtaq and Ajaz Hussain Mir, “Digital Image Forgeries and Passive Image Authentication Techniques: A Survey” International Journal of Advanced Science and Technology (IJAST), 2014, Vol.73, Pp 15-32.
  3. Ketan S Bacchuwar, Aakashdeep, K.R Ramakrishnan, “A Jump Patch-Block Match Algorithm for Multiple Forgery Detection” IEEE, 2013, Pp 723-726.
  4. Ghulam Muhammada, Muhammad Hussain , George Bebis , “Passive Copy Move Image Forgery Detection using Undecimated Dyadic Wavelet Transform” Digital Investigation, 2012,Vol. 9,Pp 49-57.
  5. Sondos M. Fadl , Noura A. Semary, Mohiy M. Hadhoud, “Copy-Rotate-Move Forgery Detection Based on Spatial Domain” IEEE,2014,Pp 136-141.
  6. Cheng-Shian Lin and Jyh-Jong Tsay, ” Passive Forgery Detection for JPEG Compressed Image based on Block Size Estimation and Consistency Analysis” Natural Science Publishing Cor.,2015, Pp 1015-1028.
  7. Michael Zimba,Sun Xingming, “DWT-PCA (EVD) Based Copy-move Image Forgery Detection” IJDCTA, Vol.5, 2011, Pp 251-258 .
  8. Ghulam Muhammad , Munner H, Al-Hammadi , Muhammad Hussain, George Bebis , “Image Forgery Detection using Steerable Pyramid Transform and Local Binary Pattern” Springer-Verlag Berlin Heidelberg ,2013.
  9. Giovanni Chierchia, Giovanni Poggi, Carlo Sansone, ”A Bayesian-MRF Approach for PRNU-based Image Forgery Detection” IEEE, 2013. Pp 1-14.
  10. Tiziano Bianchi, Alessia De Rosa, Alessandro Piva, “Improved DCT Coefficient Analysis For Forgery Localization In JPEG Images” IEEE, 2011. Pp 2444-2447.
  11. Weiqi Luo, Zhenhua Qu, Jiwu Huang, Guoping Qiu, “A Novel Method For Detecting Cropped And Recompressed Image Block” IEEE, 2007, Pp 217-220.
  12. F. Battisti, M. Carli, A. Neri , “Image Forgery Detection by using No-Reference quality metrics”
  13. M. Barni, A. Costanzo and L. Sabatini, "Identification of Cut and paste Tampering by means of Double-JPEG Detection and Image Segmentation" IEEE, 2010, Pp 1687-1690.
  14. Zhang, Zhen, Zhou Yu and BaiNa Su., "Detection of Composite Forged Image" IEEE Int. Conf. Computer Application and System Modeling (ICCASM), Taiyuan, Oct. 2010, Vol. 11 , Pp 572-576.
  15. H. Farid, "Image Forgery Detection" Signal Processing Magazine, IEEE, March 2009,Vol. 26, No. 2, Pp 16-25.
  16. [G. Cao, Y. Zhao, R. Ni and X. Li, “Contrast Enhancement-Based Forensics in Digital Images” IEEE Transactions on Information Forensics and Security, 2014,Vol 9, No. 3, Pp 515-525.
  17. G. Chierchia, G. Poggi, C. Sansone and L. Verdoliva, “A Bayesian-MRF Approach for PRNU-Based Image Forgery Detection” Information Forensics and Security, IEEE Transactions, 2014, Vol. 9, No. 4, Pp 554-567.
  18. G. K. Birajdar and V. H. Mankar, “Digital Image Forgery Detection using Passive Techniques: A survey” Digital investigations, 2013, Pp. 226-245.
  19. P. Xunyu and L. Siwei, “Region Duplication Detection using Image Feature Matching”, IEEE Trans on Information Forensics and Security, 2011, Vol. 5, No. 4, Pp 857–67.
  20. P. Kakar and N. Sudha, “Exposing Post Processed Copy-paste Forgeries through Transform-invariant Features”, IEEE Trans. on Information Forensics and Security, 2012, Vol. 7, No. 3,Pp 1018–28.
Index Terms

Computer Science
Information Sciences

Keywords

Image Forgery Feature Extraction clustering.