Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 19, pp 26033–26053 | Cite as

Image forensics using color illumination, block and key point based approach

  • Abhishek Thakur
  • Neeru Jindal
Article
First Online:

Abstract

Every individual is keen to exhibit socialism and connectedness posting their personal photos and videos on several social websites. Thus, it has become literally easy for the onlookers to see and modify their photos and videos. Here the concept enumerates in picture as image forensics, whereby it is possible to examine the authenticity and genuineness of photograph and video into consideration. In addition to this, nowadays photograph and videos are considered as a firm and valid proof in the court room for investigation, validation and judgement. Several experts are continuously working in an image forensic field to discover and develop better techniques for the detection of forgeries in image and videos. Detection of image forgeries is done in two ways. Firstly the forged image we are already familiar with is called active forgery detection technique and secondly, where we don’t know the forgery, then is referred to as the passive forgery detection technique. Passive technique is incorporated to detect forgery in this paper where hybridization is used. We have used DWT, color illumination Algorithm, SLIC Algorithm; SIFT Algorithm, Correlation Coefficient Map generation Algorithm, Block Matching Threshold Algorithm and Feature Extraction Algorithm for the detection and ramifying forgeries. The novelty of the proposed hybrid technique is the use of color illumination which detect image edges and trace them correctly to detect forged region. We have tested 48 images from database and find out image forgery detection at image level with Precision = 97. 25%; Recall = 100% and F1 = 98. 53%.

Keywords

Digital signal processing (DSP) Image forgery (IF) Discrete wavelet transform (DWT) Simple linear iterative clustering (SLIC) Block matching threshold (BMT) Feature extraction (FE) 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Abdul Wahab AW, Aminu Bagiwa M, Idna Idris MY, Khan S, Razak Z (2014) Passive Video Forgery Detection Techniques: A Survey. IEEE International Conference on Information Assurance and Security (lAS) 29–34Google Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A SIFT based forensic method for copy move attack detection and transformation recovery. IEEE Trans Inf Forensics Security 6(3):1099–1110.  https://doi.org/10.1109/TIFS.2011.2129512 CrossRefGoogle Scholar
  3. 3.
    Ansari MD, Ghrera SP, Tyagi V (2014) Pixel-Based Image Forgery Detection: A Review. IETE Journal of Education 55(1):40–46CrossRefGoogle Scholar
  4. 4.
    Anuja Dixit J, Gupta RK (2016) Copy Move Image Forgery Detection using Frequency based Techniques: A Review. International Journal of Signal Processing, Image Processing and Pattern Recognition 9(3):71–88CrossRefGoogle Scholar
  5. 5.
    Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Transactions on Image Processing. PP(99):1–40.  https://doi.org/10.1109/TIP.2010.2046599
  6. 6.
    Bashar MK, Noda K, Ohnishi N, Mori K (2016) Exploring Duplicated Regions in Natural Images. IEEE Trans Image Process 99:1–40Google Scholar
  7. 7.
    Bayram S, Sencar HT, Memon N (2009) An Efficient and Robust Method for Detecting Copy Move Forgery. Acoustics, Speech and Signal Processing.  https://doi.org/10.1109/ICASSP.2009.4959768
  8. 8.
    Birajdara GK, Mankar VH (2013) Digital image forgery detection using passive techniques: A survey. Elsevier, Digital Investigation 10:226–245CrossRefGoogle Scholar
  9. 9.
    Bo X, Junwen W, Guangjie I, Yuewei D (2010) Image copy move forgery detection based on SURF. In Proc. Int. Conf. Multimedia Inf. Netw. Secur pp. 889–892.  https://doi.org/10.1109/MINES.2010.189
  10. 10.
    Bravo Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Process pp. 1880–1883.  https://doi.org/10.1109/ICASSP.2011.5946873
  11. 11.
    de Carvalho TJ, Riess C, Pedrini H, de Rezende Rocha A (2013) Exposing Digital Image Forgeries by Illumination Color Classification. IEEE Transactions On Information Forensics And Security 8(7):1182–1194CrossRefGoogle Scholar
  12. 12.
    Carvalho T, Faria FA, Pedrini H (2016) Illuminant-Based Transformed Spaces for Image Forensics. IEEE transactions on information forensics and security 11(4):720–733CrossRefGoogle Scholar
  13. 13.
    Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy move forgery detection approaches. IEEE Trans Inf Forensics Security 7(6):1841–1854CrossRefGoogle Scholar
  14. 14.
    Dhania VS, Harish Binu KP (2016) Exposing Digital Image Forgeries Using Feature Extraction and Adaptive Over Segmentation International Journal of Innovative Research in Science. Eng Technol 5(8):14723–14729Google Scholar
  15. 15.
    Fridrich J, Soukal D, Lukas J (2003) Detection of copy-move forgery in digital images. In Proc. of the Digital Forensic Research Workshop pp. 55–61Google Scholar
  16. 16.
    Ghorbani M, Firouzmand M, Faraahi A (2011) DWT-DCT (QCD) based copymove image forgery detection. In Proc. of the 18th International Conference on Systems, Signals and Image Processing pp. 1–4Google Scholar
  17. 17.
    Guohui LI, Qiong WI, Dan TI, SunI S (2007) A Sorted Neighborhood Approach For Detecting Duplicated Regions In Image Forgeries Based On Dwt And Svd. IEEE International Conference on Multimedia and Expo:1750–1753.  https://doi.org/10.1109/ICME.2007.4285009
  18. 18.
    Hassaballah M, Abdelmgeid AA, Alshazly HA (2016) Image Features Detection, Description and Matching. In: Image Feature Detectors and Descriptors. Studies in Computational Intelligence 630:11–45Google Scholar
  19. 19.
    Luo W, Huang J (2006) Robust Detection of Region Duplication Forgery in Digital Image. 18th International Conference on Pattern Recognition (ICPR'06) 4:746–749.  https://doi.org/10.1109/ICPR.2006.1003 Google Scholar
  20. 20.
    Mahdian B, Saic S (2007) Detection of copy move forgery using a method based on blur moment invariants. Elsevier, Forensic Science International 171:180–189CrossRefGoogle Scholar
  21. 21.
    Muhammad G, Hussain M, Khawaji K, Bebis G (2011) Blind copy move image forgery detection using dyadic undecimated wavelet transform. In Proc. of the 17th International Conference on Digital Signal Processing.  https://doi.org/10.1109/ICDSP.2011.6004974
  22. 22.
    Nguyen HC, Katzenbeisser S (2012) Detection of copy-move forgery in digital images using radon transformation and phase correlation. In Proc. of the 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing pp. 134–137.  https://doi.org/10.1109/IIH-MSP.2012.38
  23. 23.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Security 5(4):857–867.  https://doi.org/10.1109/TIFS.2010.2078506 CrossRefGoogle Scholar
  24. 24.
    Pun CM, Yuan X-C, Bi X-L (2015) Image Forgery Detection Using Adaptive Over segmentation and Feature Point Matching. IEEE Transactions On Information Forensics And Security 10(8):1705–1716CrossRefGoogle Scholar
  25. 25.
    Redi JA, Taktak W, Dugelay J-L (2010) Digital image forensics: a booklet for beginners. Multimed Tools Appl Springer 51:133–162CrossRefGoogle Scholar
  26. 26.
    Sekeh MA, Maarof MA, Rohani MF, Mahdian B (2013) Efficient image duplicated region detection model using sequential block clustering. Digit Investig 10:73–84CrossRefGoogle Scholar
  27. 27.
    Shivkumar BL, Baboo SS (2011) Detection of region duplication forgery in image using SURF. IJCSI Int J Comput Sci 8(4):199–205Google Scholar
  28. 28.
    Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD - New Database for Copy Move Forgery Detection. 55th International Symposium, ELMAR. IEEE, Zadar, pp 49–54Google Scholar
  29. 29.
    Upase SG, Kuntawar SV (2016) Copy-Move Detection of Image Forgery by using DWT and SIFT Methodologies. Int J Comput Appl 148(7):37–39Google Scholar
  30. 30.
    Wang J, Liu G, Li H, Dai Y, Wang Z (2009) Detection of image region duplication forgery using model with circle block. In Proc. Int. Conf. Multimedia Inf. Netw. Secur. Pp. 25–29.  https://doi.org/10.1109/MINES.2009.142
  31. 31.
    Wang JW, Liu GJ, Zhang Z, Dai YW, Wang ZQ (2009) Fast and robust forensics for image region duplication forgery. Acta Automat Sin 35(12):1488–1495CrossRefGoogle Scholar
  32. 32.
    Wang Y, Gurule K, Wise J, Zheng J (2012) Wavelet based region duplication forgery detection. In Proc. of the 9th International Conference on Information Technology pp. 30–35,  https://doi.org/10.1109/ITNG.2012.13
  33. 33.
    van de Weijer J, Gevers T, Gijsenij A (2007) Edge based color constancy. IEEE Trans Image Process 16(9):2207–2214.  https://doi.org/10.1109/TIP.2007.901808 MathSciNetCrossRefGoogle Scholar
  34. 34.
    XiaoBing KANG, ShengMin WEI (2008) Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics. International Conference on Computer Science and Software Engineering 3:926–930.  https://doi.org/10.1109/CSSE.2008.876 Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ECEDThapar UniversityPatialaIndia

Personalised recommendations

Cite article

Buy options