Towards Drug Counterfeit Detection Using Package Paperboard Classification

  • Christof Kauba
  • Luca Debiasi
  • Rudolf Schraml
  • Andreas Uhl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9917)


Most approaches for product counterfeit detection are based on identification using some unique marks or properties implemented into each single product or its package. In this paper we investigate a classification approach involving existing packaging only in order to avoid higher production costs involved with marking each individual product. To detect counterfeit packages, images of the package’s interior showing the plain structure of the paperboard are captured. Using various texture features and SVM classification we are able to distinguish drug packages coming from different manufacturers and also forged packages with high accuracy while a distinction between single packages of the same manufacturer is not possible.


Drug counterfeit detection Paper structure classification Texture classification 



This work is supported by the Munich based software venture eMundo which receives funding from the Central Innovation Program for SMEs by Germany’s Federal Ministry of Economics and Technology (project “FakeFinder” no. ZIM-EP150145).


  1. 1.
    Buchanon, J., Cowburn, R., Jausovec, A.-V., Petit, S.: Forgery: fingerprinting documents and packaging. Nature 436(7050), 475 (2005)CrossRefGoogle Scholar
  2. 2.
    Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  3. 3.
    Clarkson, W., Weyrich, T., Finkelstein, A., Heninger, N., Halderman, J.A., Felten, E.W.: Fingerprinting blank paper using commodity scanners. In: Proceedings of the 30th IEEE Symposium on Security and Privacy, pp. 301–314 (2009)Google Scholar
  4. 4.
    Diephuis, M., Voloshynovskiy, S., Holotyak, T., Stendardo, N., Keel, B.: A framework for fast and secure packaging identification on mobile phones. In: Proceedings of SPIE Photonics West, Electronic Imaging, Media Forensics and Security V, San Francisco, USA, 23 January, 2014Google Scholar
  5. 5.
    Diephuis, M., Voloshynovskiy, S., Beekhof, F.: Physical object identification based on famos microstructure fingerprinting: comparison of templates versus invariant features. In: 8th International Symposium on Image and Signal Processing and Analysis, Trieste, Italy, 4–6 September, 2013Google Scholar
  6. 6.
    Diephuis, M., Voloshynovskiy, S., Holotyak, T.: Sketchprint: physical object micro-structure identification using mobile phones. In: European Signal Processing Conference (EUSIPCO), Nice, France, 31 August - 4, September 2015Google Scholar
  7. 7.
    Kannala, J., Rahtu, E.: BSIF: binarized statistical image features. In: Proceedings of the 21st International Conference on Pattern Recognition, ICPR, Tsukuba, Japan, November 11–15, pp. 1363–1366 (2012)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Mehta, R., Egiazarian, K.: Texture classification using dense micro-block difference (DMD). In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 643–658. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-16808-1_43 Google Scholar
  10. 10.
    Metois, E., Yarin, P., Salzman, N., Smith, J.: Fiberfingerprint identification. In: Proceedings of the 3rd Workshop on Automatic Identification, New York City, USA (2002)Google Scholar
  11. 11.
    Mikkilineni, A.K., Chiang, P.-J., Ali, G.N., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Printer identification based on graylevel co-occurrence features for security and forensic applications, vol. 5681, pp. 430–440 (2005)Google Scholar
  12. 12.
    Muhammad, G.: Multi-scale local texture descriptor for image forgery detection. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1146–1151 (2013)Google Scholar
  13. 13.
    OECD and EUIPO: Trade in counterfeit and pirated goods, p. 138. OECD Publishing (2016)Google Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, pp. 582–585, October 1994Google Scholar
  15. 15.
    Ojansivu, V., Rahtu, E., Heikkila, J.: Rotation invariant local phase quantization for blur insensitive texture analysis. In: 2008 19th International Conference on Pattern Recognition, ICpPR 2008, pp. 1–4, December 2008Google Scholar
  16. 16.
    W.H. Organization. International medical products taskforce - brochure. Accessed 29 Apr 2016
  17. 17.
    Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)CrossRefGoogle Scholar
  18. 18.
    Voloshynovskiy, S., Diephuis, M., Beekhof, F., Koval, O., Keel, B.: Towards reproducible results in authentication based on physical non-cloneable functions: the forensic authentication microstructure optical set (famos). In: Proceedings of IEEE International Workshop on Information Forensics and Security, Tenerife, Spain, 2–5 December 2012Google Scholar
  19. 19.
    Wong, C.W., Wu, M.: Counterfeit detection using paper PUF and mobile cameras. In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6, November 2015Google Scholar
  20. 20.
    Wong, C.W., Wu, M.: A study on PUF characteristics for counterfeit detection. In: IEEE International Conference on Image Processing (ICIP), pp. 1643–1647, September 2015Google Scholar
  21. 21.
    Zuiderveld, K.: Graphics gems iv. chapter contrast limited adaptive histogram equalization, pp. 474–485. Academic Press Professional Inc, San Diego, CA, USA (1994)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Christof Kauba
    • 1
  • Luca Debiasi
    • 1
  • Rudolf Schraml
    • 1
  • Andreas Uhl
    • 1
  1. 1.Department of Computer SciencesUniversity of SalzburgSalzburgAustria

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