Catching Counterfeits with AI

Worked on counterfeit identification for CPG Products,would have helped to be be to quick scan products and packaging.

Catching the Fakes

By Neil Savage    Communications of the ACM, May 2021, Vol. 64 No. 5, Pages 13-14    10.1145/3453696

Counterfeiting is a big business. Nearly $509 billion of fake and pirated products were sold internationally in 2016. In that year, the latest for which data was available, counterfeit goods made up 3.3% of international trade, up from 2.5% three years earlier, according to the Organization for Economic Cooperation and Development.

That figure, which does not include domestic trade in fakes, not only means companies are losing revenue and consumers are not getting their money's worth; counterfeiting also helps fund organized crime. It exploits low-wage laborers. Because it skirts safety regulations, makers of counterfeits could use toxic materials or produce unsafe products.

Now, companies are turning to artificial intelligence (AI) to help them identify counterfeit products and stanch the flow of faux goods.

Amazon, for instance, launched Project Zero in 2019. Makers of products provide the company with data about their logos, trademarks, and other features, and Amazon's machine learning algorithm scans listings on the company's website and automatically removes those it deems fake.

Other companies have rolled out tools to allow retailers to identify fakes using smartphones with scanning attachments.

"Counterfeit goods are among the leading causes of a lot of bad things," says Lakshminarayanan Subramanian, a professor of computer science at New York University and co-founder and chief scientist at Entrupy, a New York-based company founded in 2012 that uses AI to verify the authenticity of luxury goods.

The company's system works with microscopic images of the goods in question, looking for features that are common to an authentic product but not to a fake. Those features could be in the texture of the material, the stitching, a zipper, or the way a logo has been stamped into an item. The leather of a luxury handbag, for instance, will have what appear to be peaks and valleys when viewed at a microscopic scale.

Entropy trains its AI starting with images of an area of handbags, both authentic and counterfeit, and breaks that image into smaller chunks. It then applies bag-of-words, a technique developed for natural language processing that sorts words by their frequency in a text and uses that to make inferences about the text. In the case of handbags, the "words" are small areas of structural features that might repeat or vary from place to place across the material. The computer creates a histogram showing the frequency of these visual words and how that frequency differs between real and fake goods.  ... "