A Visual Perspective for User Identification Based on Camera Fingerprint

  • Xiang Jiang
  • Shikui WeiEmail author
  • Ruizhen Zhao
  • Ruoyu Liu
  • Yufeng Zhao
  • Yao Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)


User identification is to identify the online accounts’ identity, which is a critical problem in many applications. The key problem in that is to find a typical pattern from the online accounts data. Different from the previous works, we identify users based on the camera fingerprint. The underlying idea is that the accounts belonging to the same individual contain the photos taken by the same cameras. Based on that, we propose a new framework to deal with the user identification problem. Specifically, the camera feature of each image is extracted by the PRNU (Photo Response Non-Uniformity) algorithm. With the proposed hierarchical clustering approach, the camera features of different camera source can be achieved. Extensive experiments on a collected photo dataset show that the proposed framework is effective for identifying users, especially in the scenarios that users have multiple cameras and photos forwarding behavior.


User identification Camera fingerprint Multiple cameras Reposted images 


  1. 1.
    Bertini, F., Sharma, R., Iannì, A., Montesi, D.: Profile resolution across multilayer networks through smartphone camera fingerprint. In: Proceedings of the 19th International Database Engineering & Applications Symposium, pp. 23–32. ACM (2015)Google Scholar
  2. 2.
    Castiglione, A., Cattaneo, G., Cembalo, M., Petrillo, U.: Experimentations with source camera identification and Online Social Networks. J. Ambient Intell. Humaniz. Comput. 4(2), 265–274 (2013)CrossRefGoogle Scholar
  3. 3.
    Chang-tsun, L.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  4. 4.
    Chen, M., Fridrich, J., Goljan, M., Lukáš, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)CrossRefGoogle Scholar
  5. 5.
    Hao, H., Zhang, X., Yong, S.: Identifying evolving groups in dynamic multi-mode networks. Microcomput. Appl. 24(1), 72–85 (2011)Google Scholar
  6. 6.
    Kimura, M., Saito, K., Nakano, R., Motoda, H.: Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Disc. 20(1), 70 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Korula, N., Lattanzi, S.: An efficient reconciliation algorithm for social networks. Proc. VLDB Endowment 7(5), 377–388 (2014)CrossRefGoogle Scholar
  8. 8.
    Kumar, S., Zafarani, R., Liu, H.: Understanding user migration patterns in social media. In: AAAI Conference on Artificial Intelligence (2012)Google Scholar
  9. 9.
    Liu, S., Wang, S., Zhu, F.: Structured learning from heterogeneous behavior for social identity linkage. IEEE Trans. Knowl. Data Eng. 27(7), 2005–2019 (2015)CrossRefGoogle Scholar
  10. 10.
    Lukáš, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  11. 11.
    Marra, F., Poggi, G., Sansone, C., Verdoliva, L.: Blind prnu-based image clustering for source identification. IEEE Trans. Inf. Forensics Secur. PP(99), 1 (2017)Google Scholar
  12. 12.
    Narayanan, A., et al.: On the feasibility of internet-scale author identification. In: 2012 IEEE Symposium on Security and Privacy, pp. 300–314 (2012)Google Scholar
  13. 13.
    Perito, D., Castelluccia, C., Kaafar, M.A., Manils, P.: How unique and traceable are usernames? In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 1–17. Springer, Heidelberg (2011). Scholar
  14. 14.
    Phan, Q.T., Boato, G., Natale, F.G.B.D.: Accurate and scalable image clustering based on sparse representation of camera fingerprint. IEEE Trans. Inf. Forensics Secur. PP(99), 1Google Scholar
  15. 15.
    Phan, Q.T., Boato, G., Natale, F.G.B.D.: Image clustering by source camera via sparse representation. In: International Workshop on Multimedia Forensics and Security (2017) Google Scholar
  16. 16.
    Zafarani, R., Liu, H.: Connecting Users across Social Media Sites : A behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 41–49. ACM (2013)Google Scholar
  17. 17.
    Zafarani, R., Tang, L., Liu, H.: User identification across social media. ACM Trans. Knowl. Discov. Data 10(2), 16 (2015)CrossRefGoogle Scholar
  18. 18.
    Zhou, X., Liang, X., Zhang, H., Ma, Y.: Cross-platform identification of anonymous identical users in multiple social media networks. IEEE Trans. Knowl. Data Eng. 28(2), 411–424 (2016)CrossRefGoogle Scholar
  19. 19.
    Zhuang, J., Mei, T., Hoi, S.C.H., Hua, X.S., Zhang, Y.: Community discovery from social media by low-rank matrix recovery. ACM Trans. Intell. Syst. Technol. 5(4), 1–19 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiang Jiang
    • 1
    • 2
  • Shikui Wei
    • 1
    • 2
    Email author
  • Ruizhen Zhao
    • 1
    • 2
  • Ruoyu Liu
    • 1
    • 2
  • Yufeng Zhao
    • 3
  • Yao Zhao
    • 1
    • 2
  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina
  3. 3.Institute of Clinic Basic MedicineChina Academy of Chinese Medical SciencesBeijingChina

Personalised recommendations