Counterfeit Detection by Extracting Rules from Product Traces

L. Wang, N. Oertel, E. Müller, and T. Seidl (Germany)


Anticounterfeiting, product tracking, sequential rule, fre quent pattern, data mining


Many product authentication approaches have been devel oped to detect counterfeits in the supply chain. In this pa per, we propose a novel approach by extracting sequential rules from the tracking data of genuine products and use them to verify products with unknown authenticity. In real ity, the supply chains can be very complex and dynamic, so it is very important that the proposed approach provides a succinct and flexible representation of the detection model. It can achieve high accuracy even when the supply chain changes during operation.

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