Counterfeits on dark markets: a measurement between Jan-2014 and Sep-2015

F. Soldner*, B. Kleinberg, S.D. Johnson

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
59 Downloads (Pure)

Abstract

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. Estimations in the literature are mostly based on border seizures, but in this paper, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types to those found in seizures but that the share of watches is higher while the share of electronics, clothes, shoes, and Tobacco is lower on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the equivalent products on the surface web appear to be advertised for a multiple of the prices found for darknet markets. We provide some suggestions on how information about darknet market counterfeits could be used by companies and authorities for preventative purposes, showing that insight gathering from the dark web is valuable and could be a cost-effective alternative (or compliment) to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better.
Original languageEnglish
Article number18
Number of pages19
JournalCrime Science
Volume12
Issue number18
DOIs
Publication statusPublished - 2023

Keywords

  • Crime science
  • Fakes
  • Forgeries
  • Machine learning
  • Nlp
  • Seized goods

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