Enhancing Image Forgery Detection Using 2-D Cross Products

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 484)


The availability of sophisticated, easy-to-use image editing tools means that the authenticity of digital images can no longer be guaranteed. This chapter proposes a new method for enhancing image forgery detection by combining two detection techniques using a 2-dimensional cross product. Compared with traditional approaches, the method yields better detection results in which the tampered regions are clearly identified. Another advantage is that the method can be applied to enhance a variety of detection algorithms. The method was tested on the CASIA TIDE v2.0 public dataset of color images and the results compared against those obtained using the re-interpolation, JPEG noise quantization and noise estimation techniques. The experimental results indicate that the proposed method is efficient and has superior detection characteristics.


Image tampering Forgery detection Cross product 


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  1. 1.
    Birajdar, G., Mankar, V.: Digital image forgery detection using passive techniques: A survey. Digital Investigation 10(3), 226–245 (2013)CrossRefGoogle Scholar
  2. 2.
    Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic Science International 214(1–3), 33–43 (2012)CrossRefGoogle Scholar
  3. 3.
    Chinese Academy of Sciences Institute of Automation (CAS- IA), CASIA v2.0, Beijing, China (2016). forensics.idealtest.org/casiav2
  4. 4.
    Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Transactions on Information Forensics and Security 4(1), 154–160 (2009)CrossRefGoogle Scholar
  5. 5.
    Gallagher, A.: Detection of linear and cubic interpolation in JPEG compressed images. In: Proceedings of the Second Canadian Conference on Computer and Robot Vision, pp. 65–72 (2005)Google Scholar
  6. 6.
    Hwang, M., Har, D.: A novel forged image detection method using the characteristics of interpolation. Journal of Forensic Sciences 58(1), 151–162 (2013)CrossRefGoogle Scholar
  7. 7.
    Kaur, M., Jyoti: Image tamper detection using non alignment of JPEG grids. International Journal of Emerging Technologies in Computational and Applied Sciences 6(4), 331–333 (2013)Google Scholar
  8. 8.
    Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image and Vision Computing 27(10), 1497–1503 (2009)CrossRefGoogle Scholar
  9. 9.
    Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Processing: Image Communication 25(6), 389–399 (2010)Google Scholar
  10. 10.
    Pan, X., Zhang, X., Lyu, S.: Exposing image forgery with blind noise estimation. In: Proceedings of the Thirteenth ACM Workshop on Multimedia and Security, pp. 15–20 (2011)Google Scholar
  11. 11.
    Popescu, A., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing 53(10), 3948–3959 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Talmale, G., Jasutkar, R.: Analysis of different techniques of image forgery detection. IJCA Proceedings on National Conference on Recent Trends in Computing 2012(3), 13–18 (2012)Google Scholar
  13. 13.
    Yun, Y., Lee, J., Jung, D., Har, D., Choi, J.: Detection of digital forgeries using an image interpolation from digital images. In: Proceedings of the IEEE International Symposium on Consumer Electronics (2008)Google Scholar
  14. 14.
    Zhang, Z., Zhou, Y., Kang, J., Ren, Y.: Study of image splicing detection. In: Proceedings of the Fourth International Conference on Intelligent Computing, pp. 1103–1110 (2008)Google Scholar
  15. 15.
    Zhao, Y., Liao, M., Shih, F., Shi, Y.: Tampered region detection of inpainted JPEG images. Optik - International Journal for Light and Electron Optics 124(16), 2487–2492 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Information Science and EngineeringRitsumeikan UniversityShigaJapan

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