Counterfeit Product Detection Analysis and Prevention as Well as Prepackage Coverage Assessment Using Machine Learning

  • Aradhana BehuraEmail author
  • Ashutosh BehuraEmail author
  • Himansu DasEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


There is no deny in the fact that duplicate product jeopardizes the luck of businesses worldwide over violating patent rights and causing immense commercial wound. Hence, it has to change into basic for firms to ensure their disgraces opposed to duplication. Existing technologies for electrical forged find encompass the applying of extra security looks prefer Watermark Technology to the emblem stock itself that raises the charges of one’s merchandise. In this paper, a reliable method for counterfeit prediction is proposed. This method can be used by customer to predict counterfeit for their daily need items available in the market. The item may be medicine, detergent or food packet. Now the development of new package of the product always comes with the risk of counterfeiting, sometimes that could affect our health, company reputation and goodwill. We have used the concept of invisible and visible watermarking present in item itself to provide authenticity of the product. This is a low cost solution that help enterprises and consumers identify the authenticity of products.


Boosting Product detection Feature extraction Discrete Wavelet Transform (DWT) 


  1. 1.
    Najafi, E.: A robust embedding and blind extraction of image watermarking based on discrete wavelet transform. Department of Mathematics, Faculty of Science, Urmia University, Iran. Math. Sci.11, 307–318 (2017). 10.1007/s40096-017-0233-1. SpringerGoogle Scholar
  2. 2.
    Averbuch, A., Lazar, D., Israeli, M.: Image compression using wavelet transform and decomposition. IEEE Trans. Image Process. 5(1) 2017Google Scholar
  3. 3.
    Nandi, S., Roy, S., Nath, S., Chakraborty, S., Karaa, W.B.A., Dey, N.: 1-D group cellular automata based image encryption technique. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (2014)Google Scholar
  4. 4.
    Horn, C., Blankenburg, M.: Automated Detection of Counterfeit Consumer Goods using Product. IWF, Technische University at Berlin, Pascalstrasse 8, 10587 BerlinGoogle Scholar
  5. 5.
    Drews Jr., P.L.J., Nascimento, E.R., Botelho, S.S.C., Campos, M.F.M.: Underwater depth estimation and image restoration based on single images. IEEE Comput. Graph. Appl. 36(2), 24–35 (2016)CrossRefGoogle Scholar
  6. 6.
    Yeung, M.M., Mintzer, F.: An invisible watermarking technique for image verification. IEEE Trans. Consum. Electron. 39, 93–103 (2007)Google Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson International Edition, London, 2008. Google Scholar
  8. 8.
    Lin, P.-Y., Chen, Y.-H., Chang, C.-C., Lee, J.-S.: Contrast-Adaptive Removable Visible Watermarking (CARVW) mechanism. Image Vis. Comput. (2013)Google Scholar
  9. 9.
    OECD: The Economic Impact of Counterfeiting and Piracy, OECD, Paris (2008). www.oecd
  10. 10.
    Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. (2018) Google Scholar
  11. 11.
    Sahani, R., Rout, C., Badajena, J.C., Jena, A.K., Das, H.: Classification of intrusion detection using data mining techniques. In: Progress in Computing, Analytics and Networking, pp. 753–764. Springer, Singapore (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVSSUTBurlaIndia
  2. 2.Department of Computer Science and EngineeringKIIT UniversityBhubaneswarIndia
  3. 3.School of Computer EngineeringKIIT Deemed to be UniversityBhubaneswarIndia

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