Detection of Image Forgery Based on Improved PCA-SIFT

  • Kunlun Li
  • Hexin Li
  • Bo Yang
  • Qi Meng
  • Shangzong Luo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 277)

Abstract

In view of the problem existing in abusive using of image copy-move forgeries, this paper proposes an image forensics algorithm for detecting copy-move forgery based on improved PCA-SIFT. The present method works first by extracting features of an image and then reducing its dimensionality, and the method uses k-nearest neighbor to operate forgery detection. Owing to the similarity between pasted region and copied region, the descriptors are then matched between each other to seek for any possible forgery in images. Extensive experimental results are presented to confirm that the algorithm is able to precisely individuate the tampered image and quantify its robustness and sensitivity to image post-processing and offer a considerable improvement in time efficiency.

References

  1. 1.
    Gavin, L., Shih, F. Y., & Liao, H.-Y. M. (2013). An efficient expanding block algorithm for image copy-move forgery detection. Information Sciences, 239, 253–265.CrossRefGoogle Scholar
  2. 2.
    Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012). An evaluation of popular copy-move forgery detection approaches. Information Forensics and Security, 7(6), 1841–1854.CrossRefGoogle Scholar
  3. 3.
    Jessica, Fridrich., David, Soukal., Jan, Lukas. (2003). Detection of copy–move forgery in digital images. Proceedings of Digital Forensic Research Workshop (DFRWS’03) (pp. 55–61). Cleveland, OH: IEEE Computer Society.Google Scholar
  4. 4.
    Babak, M., & Stanislav, S. (2007). Detection of copy–move forgery using a method based on blur moment invariants. Forensic Science International, 171(2–3), 180–189.Google Scholar
  5. 5.
    Mohammad Akbarpour, S., Mohd. Aizaini, M., Mohd. Foad, R., & Babak, M. (2013). Efficient image duplicated region detection model using sequential block clustering. Digital Investigation, 10(1), 73–84.CrossRefGoogle Scholar
  6. 6.
    Hailing, Huang., Weiqiang, Guo., Yu, Zhang. (2008). Detection of copy-move forgery in digital images using SIFT algorithm. Proceedings of IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA’08) (vol. 2, pp. 272–276). Wuhan: IEEE Computer Society.Google Scholar
  7. 7.
    Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  8. 8.
    Yan, Ke., Rahul, Sukthankar. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of 2004 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04) (vol. 2, pp. 506–513). Washington, DC: IEEE Computer Society.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kunlun Li
    • 1
  • Hexin Li
    • 1
  • Bo Yang
    • 2
  • Qi Meng
    • 1
  • Shangzong Luo
    • 1
  1. 1.College of Electronic and Information EngineeringHebei UniversityBaodingChina
  2. 2.College of Mechanical EngineeringYanshan UniversityQinhuangdaoChina

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