A Classification Engine for Image Ballistics of Social Data

  • Oliver GiudiceEmail author
  • Antonino Paratore
  • Marco Moltisanti
  • Sebastiano Battiato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)


Image Forensics has already achieved great results for the source camera identification task on images. Standard approaches for data coming from Social Network Platforms cannot be applied due to different processes involved (e.g., scaling, compression, etc.). In this paper, a classification engine for the reconstruction of the history of an image, is presented. Specifically, machine learning techniques and a-priori knowledge acquired through image analysis, we propose an automatic approach that can understand which Social Network Platform has processed an image and the software application used to perform the image upload. The engine makes use of proper alterations introduced by each platform as features. Results, in terms of global accuracy on a dataset of 2720 images, confirm the effectiveness of the proposed strategy.


Social networks Image forensics JPEG Digital ballistic 


  1. 1.
    Piva, A.: An overview on image forensics. ISRN Sig. Process. 2013, 22 (2013)Google Scholar
  2. 2.
    Stamm, M.C., Wu, M., Liu, K.J.R.: Information forensics: an overview of the first decade. IEEE Access 1, 167–200 (2013)CrossRefGoogle Scholar
  3. 3.
    Battiato, S., Giudice, O., Paratore, A.: Multimedia forensics: discovering the history of multimedia contents. In: Proceedings of the 17th International Conference on Computer Systems and Technologies 2016, pp. 5–16 ACM (2016)Google Scholar
  4. 4.
    Bruna, A.R., Messina, G., Battiato, S.: Crop detection through blocking artefacts analysis. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6978, pp. 650–659. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24085-0_66 CrossRefGoogle Scholar
  5. 5.
    Luo, W., Qu, Z., Huang, J., Qiu, G.: A novel method for detecting cropped and recompressed image block. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. II.217–II.220, April 2007Google Scholar
  6. 6.
    Battiato, S., Farinella, G.M., Messina, E., Puglisi, G.: Robust image alignment for tampering detection. IEEE Trans. Inf. Forensics Secur. 7(4), 1105–1117 (2012)CrossRefGoogle Scholar
  7. 7.
    Kee, E., Johnson, M.K., Farid, H.: Digital image authentication from JPEG headers. IEEE Trans. Inf. Forensics Secur. 6(3), 1066–1075 (2011)CrossRefGoogle Scholar
  8. 8.
    Kee, E., Farid, H.: Digital image authentication from thumbnails. In: IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, p. 75 410E (2010)Google Scholar
  9. 9.
    Gloe, T.: Forensic analysis of ordered data structures on the example of JPEG files. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 139–144, December 2012Google Scholar
  10. 10.
    Chen, Y., Thing, V.L.: A study on the photo response non-uniformity noise pattern based image forensics in real-world applications. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p. 1 (2012)Google Scholar
  11. 11.
    Dirik, A.E., Karaküçük, A.: Forensic use of photo response non-uniformity of imaging sensors and a counter method. Opt. Express 22(1), 470–482 (2014)CrossRefGoogle Scholar
  12. 12.
    Redi, J.A., Taktak, W., Dugelay, J.-L.: Digital image forensics: a booklet for beginners. Multimedia Tools Appl. 51(1), 133–162 (2011)CrossRefGoogle Scholar
  13. 13.
    Galvan, F., Puglisi, G., Bruna, A.R., Battiato, S.: First quantization matrix estimation from double compressed JPEG images. IEEE Trans. Inf. Forensics Secur. 9(8), 1299–1310 (2014)CrossRefGoogle Scholar
  14. 14.
    Battiato, S., Messina, G.: Digital forgery estimation into DCT domain: a critical analysis. In: Proceedings of the First ACM Workshop on Multimedia in Forensics, ser. MiFor 2009, pp. 37–42. ACM, New York (2009)Google Scholar
  15. 15.
    Farid, H.: Digital image ballistics from JPEG quantization: a followup study. Department of Computer Science, Dartmouth College, Techncial report TR2008-638 (2008)Google Scholar
  16. 16.
    Kornblum, J.D.: Using JPEG quantization tables to identify imagery processed by software. Digital Invest. 5, S21–S25 (2008). The Proceedings of the Eighth Annual DFRWS ConferenceCrossRefGoogle Scholar
  17. 17.
    Goljan, M., Chen, M., Comesaña, P., Fridrich, J.: Effect of compression on sensor-fingerprint based camera identification. Electron. Imaging 2016(8), 1–10 (2016)CrossRefGoogle Scholar
  18. 18.
    Rosenfeld, K., Sencar, H.T.: A study of the robustness of PRNU-based camera identification. In: IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, p. 72 540M (2009)Google Scholar
  19. 19.
    Moltisanti, M., Paratore, A., Battiato, S., Saravo, L.: Image manipulation on facebook for forensics evidence. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9280, pp. 506–517. Springer, Cham (2015). doi: 10.1007/978-3-319-23234-8_47 CrossRefGoogle Scholar
  20. 20.
    Castiglione, A., Cattaneo, G., Santis, A.D.: A forensic analysis of images on online social networks. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 679–684 (2011)Google Scholar
  21. 21.
    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
  22. 22.
    DJPEG - LibJPEG open-source project on GITHUB.
  23. 23.
    Miano, J.: Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP. Addison-Wesley Professional, Boston (1999)Google Scholar
  24. 24.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Oliver Giudice
    • 1
    Email author
  • Antonino Paratore
    • 2
  • Marco Moltisanti
    • 1
  • Sebastiano Battiato
    • 1
  1. 1.Image Processing LaboratoryUniversity of CataniaCataniaItaly
  2. 2.iCTLab s.r.l.Spin-off of University of CataniaCataniaItaly

Personalised recommendations