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Provenance Analysis for Instagram Photos

  • Yijun QuanEmail author
  • Xufeng Lin
  • Chang-Tsun Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than \(96\%\) in differentiating the filters in Group I and Group II.

Keywords

Digital image forensics Sensor pattern noise Social media Provenance analysis 

References

  1. 1.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  2. 2.
    Chen, M., Fridrich, J., Goljan, M., Lukás, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)CrossRefGoogle Scholar
  3. 3.
    Hu, Y., Yu, B., Jian, C.: Source camera identification using large components of sensor pattern noise. In: Proceedings of International Conference on Computer Science and Its Applications, pp. 291–294 (2009)Google Scholar
  4. 4.
    Li, C.T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  5. 5.
    Kang, X., Li, Y., Qu, Z., Huang, J.: Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 7(2), 393–402 (2012)CrossRefGoogle Scholar
  6. 6.
    Lin, X., Li, C.T.: Preprocessing reference sensor pattern noise via spectrum equalization. IEEE Trans. Inf. Forensics Secur. 11(1), 126–140 (2016)CrossRefGoogle Scholar
  7. 7.
    Lin, X., Li, C.T.: Enhancing sensor pattern noise via filtering distortion removal. IEEE Signal Process. Lett. 23(3), 381–385 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bloy, G.J.: Blind camera fingerprinting and image clustering. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 532–534 (2007)CrossRefGoogle Scholar
  9. 9.
    Li, C.T.: Unsupervised classification of digital images using enhanced sensor pattern noise. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 3429–3432, May 2010Google Scholar
  10. 10.
    Caldelli, R., Amerini, I., Picchioni, F., Innocenti, M.: Fast image clustering of unknown source images. In: Proceedings of IEEE International Workshop on Information Forensics and Security. pp. 1–5, December 2010Google Scholar
  11. 11.
    Lin, X., Li, C.T.: Large-scale image clustering based on camera fingerprints. IEEE Trans. Inf. Forensics Secur. 12(4), 793–808 (2017)Google Scholar
  12. 12.
    Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: IS&T/SPIE Electronic Imaging, p. 72540I. International Society for Optics and Photonics (2009)Google Scholar
  13. 13.
    Gloe, T., Bhme, R.: The dresden image database for benchmarking digital image forensics. J. Digital Forensic Pract. 3(2–4), 150–159 (2010)CrossRefGoogle Scholar
  14. 14.
    Satta, R., Stirparo, P.: On the usage of sensor pattern noise for picture-to-identity linking through social network accounts. In: International Conference on Computer Vision Theory and Applications (VISAPP), vol. 3, pp. 5–11 (2014)Google Scholar
  15. 15.
    Caldelli, R., Becarelli, R., Amerini, I.: Image origin classification based on social network provenance. IEEE Trans. Inf. Forensics Secur. 12(6), 1299–1308 (2017)CrossRefGoogle Scholar
  16. 16.
    Amerini, I., Uricchio, T., Caldelli, R.: Tracing images back to their social network of origin: a CNN-based approach. In: 2017 IEEE Workshop on Information Forensics and Security (WIFS), pp. 1–6, December 2017Google Scholar
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  18. 18.
    Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)
  19. 19.
    Shullani, D., Fontani, M., Iuliani, M., Shaya, O.A.: VISION: a video and image dataset for source identification. EURASIP J. Inf. Secur. 2017(1), 15 (2017)CrossRefGoogle Scholar
  20. 20.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Li, C.T., Lin, X.: A fast source-oriented image clustering method for digital forensics. EURASIP J. Image Video Process. 1, 69–84 (2017). Special issues on image and video forensics for social media analysisCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of WarwickCoventryUK
  2. 2.Charles Sturt UniversityWagga WaggaAustralia

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