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Reseach Article

Blind Approach for Digital Image Forgery Detection

by Tulsi Thakur, Kavita Singh, Arun Yadav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 10
Year of Publication: 2018
Authors: Tulsi Thakur, Kavita Singh, Arun Yadav
10.5120/ijca2018916108

Tulsi Thakur, Kavita Singh, Arun Yadav . Blind Approach for Digital Image Forgery Detection. International Journal of Computer Applications. 179, 10 ( Jan 2018), 34-42. DOI=10.5120/ijca2018916108

@article{ 10.5120/ijca2018916108,
author = { Tulsi Thakur, Kavita Singh, Arun Yadav },
title = { Blind Approach for Digital Image Forgery Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 10 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 34-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number10/28839-2018916108/ },
doi = { 10.5120/ijca2018916108 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:00.529944+05:30
%A Tulsi Thakur
%A Kavita Singh
%A Arun Yadav
%T Blind Approach for Digital Image Forgery Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 10
%P 34-42
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the digital era of where everyone is exposed to a visual imagery in very large extent. Digital images are very convincible way to share information. Due to the rapidly growing field of digital image acquirement and editing software that are impressive as well sophisticated with many advanced features. Manipulation with features of digital image can perform easily with the help of editing tools, which are cost effectively available online or offline and do not leave any visible footprint of tampering with an image. Forgery with the digital image is an unavoidable problem concern with the image authenticity and also with image integrity. Which raising a compulsion to take an immediate action on the forgery of the digital image to verify the authenticity and maintain the integrity. To encounter the problem of authenticity of digital image, this paper proposed a methodology for detection of image splicing forgery using the blind approach i.e., passive method to detect the spliced region in the digital image. In passive approach, there is no provision for the pre-introduction of the watermark and pre-embedded digital signature during the time of image obtainment. This paper mainly concern with the image splicing forgery and it initiate with the DWT (Discrete Wavelet Transform) method, which will decompose the image into sub images and obtain coefficient for each sub image. After that for feature extraction we will use SURF (Speed-Up Robust Features) and finally SVM (Support Vector Machine) will perform classification for splicing forgery detection in digital image.

References
  1. V. P. Nampoothiri and N. Sugitha, 2016, “Digital Image Forgery - A threaten to Digital Forensics”, IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT), 1-6.
  2. C. N. Bharti and P. Tandel, 2016, “A Survey of Image Forgery Detection Techniques”, IEEE International conference on Wireless Communication, signal Processing and Networking, 877-881.
  3. A. A. Alahmadi, M. Hussain, H. Aboalsamh, G. Muhammad, G. Bebis, 2013, “Splicing Image Forgery Detection Based on DCT and Local Binary Pattern”, IEEE Global Conference on Signal and Information Processing, 253-256.
  4. S. D. Lin and T. Wu, 2011, “An Integrated Technique for Splicing and Copy-move Forgery Image Detection”, IEEE 4th International Congress on Image and Signal Processing, 1086-1090.
  5. Y. Fan, P. Carré and C. Fernandez-Maloigne, 2015, “Image Splicing Detection with Local Illumination Estimation”, IEEE International Conference on Image Processing (ICIP), 2940-2944.
  6. S. D. Mahalakshmi, K. Vijayalakshmi and E. Agnes, 2013, “A Forensic Method for Detecting Image Forgery”, IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), 590-594.
  7. P. Yadav, 2012, “Detection of Copy-Move Forgery of Images Using Discrete Wavelet Transform”, IEEE International Journal on Computer Science and Engineering (IJCSE), Vol. 4, No. 04, 565-570.
  8. A. Kashyap, R. S. Parmar, B. Suresh, M. Agarwal and H. Gupta, 2017, “Detection of Digital Image Forgery using Wavelet Decomposition and Outline Analysis”, International Conference of Signal Processing and Communication (ICSC), 187-190.
  9. T. H. Park, J. G. Han, Y. H. Moon and K. Eom, 2016, “Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain”, SpringerOpen EURASIP Journal on Image and Video Processing, 1-10.
  10. Z. Zhang, J. Kang and Y. Ren, 2008, “An Effective Algorithm of Image Splicing Detection”, IEEE International Conference on Computer Science and Software Engineering, 1035-1039.
  11. W. Wang, J. Dong and T. Tan, 2009, “Effective Image Splicing Detection Based on Image Chroma”, IEEE International Conference on Image Processing (ICIP), 1257-1260.
  12. P. Deshpande and P. Kanikar, 2012, “Pixel Based Digital Image Forgery Detection Techniques”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, No. 3, 539-543.
  13. M. M. Isaaca and M Wilscy, 2015, “Image forgery detection based on Gabor Wavelets and Local Phase Quantization”, ELSEVIER Second International Symposium on Computer Vision and the Internet (VisionNet’15), 76-83.
  14. M. F. Hashmi, A. R. Hambarde and A. G. Keskar, 2013, “Copy Move Forgery Detection using DWT and SIFT Features”, IEEE International Conference on Intelligent Systems Design and Applications (ISDA), 189-193.
  15. R. Rajkumar and K. M. Singh, 2015, “Digital Image Forgery Detection using SIFT Features”, IEEE International Symposium on Advanced Computing and Communication (ISACC), 186-191.
  16. R. M. Rad and K. Wong, 2015, “Digital Image Forgery Detection by Edge Analysis”, IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 19-20.
  17. K. Khuspe and V. Mane, 2015, “Robust Image Forgery Localization and Recognition in Copy-Move Using Bag of Features and SVM”, IEEE International Conference on Communication, Information Computing Technology (ICCICT), 1-5.
  18. A. V. Mirea, S. B. Dhokb, N. J. Mistrya and P. D. Poreya, 2015, “Factor Histogram based Forgery Localization in Double Compressed JPEG Images”, ELSEVIER Eleventh International Multi-Conference on Information Processing (IMCIP), 690-696.
  19. V. Sharma, S. Jha, R. K. Bharti, 2016 , “Image Forgery and it’s Detection Technique: A Review”, International Research Journal of Engineering and Technology (IRJET), Vol. 3, No. 03, 756-762.
  20. B Shwetha and S V Sathyanarayana, 2017, “Digital image forgery detection techniques: a survey”, ACCENTS Transactions on Information Security, Vol. 2(5), 22-31.
  21. Guohui LiI, Qiong WuI, Dan TuI and Shaojie SunI , 2007 , “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries based on DWT and SVD”, IEEE International Conference on Multimedia Expo(ICME), 1750-1753.
  22. XiaoBing Kang and ShengMin Wei, 2008, “Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics”, IEEE International Conference on Computer Science and Software Engineering, 926-930.
  23. The dataset mentioned in the paper is freely available to reader at the address [online]. Available: http://forensics.idealtest.org/casiav1/
  24. The dataset mentioned in the paper is freely available to reader at the address [online]. Available: http://forensics.idealtest.org/casiav2/
  25. The dataset mentioned in the paper is freely available to reader at the address [online]. Available: http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm
Index Terms

Computer Science
Information Sciences

Keywords

Digital image forgery Tampering detection technique Copy-move forgery Splicing forgery Image retouching DWT SVM SURF