Advertisement

A Robust and Fast Technique to Detect Copy Move Forgery in Digital Images Using SLIC Segmentation and SURF Keypoints

  • Kanica Sachdev
  • Mandeep Kaur
  • Savita Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 479)

Abstract

Digital image forgery is the alteration of an image in any form. Increasing advancements in the quality of image capturing devices and photo editing softwares have made the process of image forgery easy. Copy move forgery is the most frequent type among the various types of forgery. This paper presents a novel technique to detect copy move forgery that uses Speeded Up Robust Features (SURF) keypoints. First, the image is sectioned into nonoverlying blocks using Simple Linear Iterative Clustering (SLIC) algorithm. After this, the feature points are selected using SURF. The matching of the SURF points of different segmented regions is done using cosine similarity. The matched regions represent the forged areas. This method has a low computational complexity and shows good results even if the forged area is rotated or scaled.

Keywords

Image forgery SURF SLIC Copy move forgery 

References

  1. 1.
    Bayram, Sevinc, Husrev Taha Sencar, and Nasir Memon. “A survey of copy-move forgery detection techniques.” In IEEE Western New York Image Processing Workshop, pp. 538–542. 2008.Google Scholar
  2. 2.
    Ali Qureshi, M., and M. Deriche. “A review on copy move image forgery detection techniques.” In Systems, Signals & Devices (SSD), 2014 11th International Multi-Conference on, pp. 1–5. IEEE, 2014.Google Scholar
  3. 3.
    Fridrich, A. Jessica, B. David Soukal, and A. Jan Lukáš. “Detection of copy-move forgery in digital images.” In Proceedings of Digital Forensic Research Workshop. 2003.Google Scholar
  4. 4.
    Popescu, A. C., and H. Farid. “Exposing digital forgeries by detecting duplicated image region [Technical Report]. 2004-515.” Hanover, Department of Computer Science, Dartmouth College. USA (2004).Google Scholar
  5. 5.
    Bayram, Sevinc, Husrev Taha Sencar, and Nasir Memon. “An efficient and robust method for detecting copy-move forgery.” In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pp. 1053–1056. IEEE, 2009.Google Scholar
  6. 6.
    Ryu, Seung-Jin, Min-Jeong Lee, and Heung-Kyu Lee. “Detection of copy-rotate-move forgery using Zernike moments.” In Information Hiding, pp. 51–65. Springer Berlin Heidelberg, 2010.Google Scholar
  7. 7.
    Panchal, P. M., S. R. Panchal, and S. K. Shah. “A comparison of SIFT and SURF.” International Journal of Innovative Research in Computer and Communication Engineering 1, no. 2 (2013): 323–327.Google Scholar
  8. 8.
    Bo, Xu, Wang Junwen, Liu Guangjie, and Dai Yuewei. “Image copy-move forgery detection based on SURF.” In Multimedia Information Networking and Security (MINES), 2010 International Conference on, pp. 889–892. IEEE, 2010.Google Scholar
  9. 9.
    Amtullah, Salma, and Dr Ajay Koul. “Passive Image Forensic Method to detect Copy Move Forgery in Digital Images.” IOSR Journal of Computer Engineering (IOSR-JCE) 16, no. 2: 96–104.Google Scholar
  10. 10.
    Achanta, Radhakrishna, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. “SLIC superpixels compared to state-of-the-art superpixel methods.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 34, no. 11 (2012): 2274–2282.Google Scholar
  11. 11.
    Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool. “Surf: Speeded up robust features.” In Computer vision–ECCV 2006, pp. 404–417. Springer Berlin Heidelberg, 2006.Google Scholar
  12. 12.
    Vedaldi, Andrea, and Brian Fulkerson. “VLFeat: An open and portable library of computer vision algorithms.” In Proceedings of the 18th ACM international conference on Multimedia, pp. 1469–1472. ACM, 2010.Google Scholar
  13. 13.
    Tralic, Dijana, Ivan Zupancic, Sonja Grgic, and Mislav Grgic. “CoMoFoD—New database for copy-move forgery detection.” In ELMAR, 2013 55th International Symposium, pp. 49–54. IEEE, 2013.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of Computer Science, UIETPanjab UniversityChandigarhIndia
  2. 2.Department of Information Technology, UIETPanjab UniversityChandigarhIndia

Personalised recommendations