Published March 26, 2021 | Version V1
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Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting ( datasets archive part 2/2)

  • 1. University of Warwick
  • 2. Durham University

Description

Including the variation test datasets of denomination, substrate and equipment.

Abstract—Polymer banknotes are the trend for printed currency and have been adopted by more than fifty countries worldwide. However, over the past years, the quantity of polymer counterfeits has been increasing, so has the quality of counterfeits. This shows that the initial advantage of bringing a new polymer technology to fight against counterfeiting is reducing. To maintain one step ahead of counterfeiters, we propose a novel anti-counterfeiting technique called Polymer Substrate Fingerprinting (PSF). Our technique is built based on the observation that the opacity coating, a critical step during the production of polymer notes, is a stochastic manufacturing process, leaving uneven thickness in the coating layer and the random dispersion of impurities from the ink. The imperfections in the coating layer result in random translucent patterns when a polymer banknote is back-lit by a light source. We show these patterns can be reliably captured by a commodity negative-film scanner and processed into a compact fingerprint to uniquely identify each banknote. Using an extensive dataset of 6,200 sample images collected from 340 UK banknotes, we show that our method can reliably authenticate banknotes, and is robust against rough daily handling of banknotes. Furthermore, we show the extracted fingerprints contain around 900 bits of entropy, which makes it extremely scalable to identify every polymer note circulated globally. As compared with previous or existing anti-counterfeiting mechanisms for banknotes, our method has a distinctive advantage: it ensures that even in the extreme case when counterfeiters have procured the same printing equipment and ink as used by a legitimate government, counterfeiting banknotes remains infeasible because of the difficulty to replicate a stochastic manufacturing process.

Files

denomination variation dataset_split.zip

Files (36.2 GB)

Name Size Download all
md5:0d6c2d5b5043c6d4638c2f556d843899
1.6 GB Download
md5:7b982b2e1ef10bfc449f8e20d1a138a1
1.6 GB Download
md5:36f6ecff0c28a3913ff4114d64af7a85
1.6 GB Download
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1.6 GB Download
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1.6 GB Download
md5:1aa0393915a6f755570cb69b95740e0e
179.7 MB Preview Download
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1.6 GB Download
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1.6 GB Download
md5:a1511e3000b5632084db40602004aefc
1.6 GB Download
md5:1d225ed3fce139fa4e2eae363e1e917a
1.6 GB Download
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1.6 GB Download
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1.6 GB Download
md5:bd3db45821cbd7877669fbb9db1d1b85
1.6 GB Download
md5:95b4d75270419ceb7482303c669ffeb0
1.6 GB Download
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1.6 GB Download
md5:1ea2791ab9be014388c6853373d032b4
374.3 MB Preview Download
md5:5849aae06428c3fade77bc0ebe641967
1.6 GB Download
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1.6 GB Download
md5:b1c4d29c8e29a9f64d489034da2309f5
1.6 GB Download
md5:002bc007524506fbc4381b5193aecbe5
1.6 GB Download
md5:84ece6eae99bc61a120df0a8f9cd34a5
1.6 GB Download
md5:7946359eeba64daa6788f11aecebafb8
1.6 GB Download
md5:6334421c066eca10e0b76f2e4726815b
1.6 GB Download
md5:11bf2a366692bd0714948d0a4b91274b
1.6 GB Download
md5:55b446c70c6683f9ec4210c76400129e
1.1 GB Preview Download

Additional details

Related works

Is supplement to
10.1109/TIFS.2021.3067440 (DOI)