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Digital-Physical Parity for Food Fraud Detection

  • Sin Kuang LoEmail author
  • Xiwei Xu
  • Chen Wang
  • Ingo Weber
  • Paul Rimba
  • Qinghua Lu
  • Mark Staples
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11521)

Abstract

Food fraud has an adverse impact on all stakeholders in the food production and distribution process. Lack of transparency in food supply chains is a strong factor contributing to food fraud. With limited transparency, the insights on food supply chains are fragmented, and every participant has to rely on trusted third parties to assess food quality. Blockchain has been introduced to the food industry to enable transparency and visibility, but it can only protect the integrity of a digital representation of physical food, not the physical food directly. Tagging techniques, like barcodes and QR codes that are used to connect the physical food to its digital representation, are vulnerable to attacks. In this paper, we propose a blockchain-based solution to link physical items, like food, to their digital representations using physical attributes of the item. This solution is generic in its support for different methods to perform the physical checks; as a concrete example, we use machine learning models on visual features of food products, through regular and thermal photos. Furthermore, we use blockchain to introduce a reward system for supply chain participants, which incentivizes honesty and supplying data. We evaluate the technical feasibility of components of this architecture for food fraud detection using a real-world scenario, including machine-learning models for distinguishing between grain-fed and grass-fed beef.

Keywords

Blockchain Machine learning Food fraud 

Notes

Acknowledgements

This research is supported by the Science and Industry Endowment Fund of Australia.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sin Kuang Lo
    • 1
    • 2
    Email author
  • Xiwei Xu
    • 1
    • 2
  • Chen Wang
    • 1
  • Ingo Weber
    • 1
    • 2
  • Paul Rimba
    • 1
  • Qinghua Lu
    • 1
    • 2
  • Mark Staples
    • 1
    • 2
  1. 1.Data61, CSIROSydneyAustralia
  2. 2.School of Computer Science and EngineeringUNSWSydneyAustralia

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