Abstract
The economy for artisanal products, such as Navajo rugs or Pashmina shawls are often threatened by mass-produced fakes. We propose the use of AI-based authentication as one part of a larger system that would replace extractive economies with generative circulation. In this case study we examine initial experiments towards the development of a cell phone-based authentication app for kente cloth in West Africa. We describe the context of weavers and cloth sales; an initial test of a machine learning algorithm for distinguishing between real and fake kente, and an outline of the next stages of development.
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Data used within this work is available at https://github.com/robinsonkwame/kente-cloth-authentication.
Notes
A case can be made that the print is not intended to be mistaken for handwoven, and therefore legitimately sold. But we have observed tourists failing to grasp the distinction, so the impact on weavers is the same. There are also distinctions between “authentic prints” made in Ghana, and those produced in foreign countries and smuggled across the border, but that is beyond the scope of this paper.
Using a mosaic layout algorithm by dvdtho available at https://github.com/dvdtho/python-photo-mosaic.
The CIE L*a*b* color space model, abbreviated as LAB, is a non-linear model based on human color perception and expresses color in terms of L*, lightness, from black (0) to white (100), a* from green to red and b* from blue to yellow.
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This research is supported by National Science Foundation (NSF) Grant# 1640014 and Mcubed Grant# 8330.
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Robinson, K.P., Eglash, R., Bennett, A. et al. Authente-Kente: enabling authentication for artisanal economies with deep learning. AI & Soc 36, 369–379 (2021). https://doi.org/10.1007/s00146-020-01055-2
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DOI: https://doi.org/10.1007/s00146-020-01055-2