Spotting Fake Handbags

This article first appeared in The Edge Financial Daily, on September 14, 2017.

Entrupy’s device, which looks like a bulky flashlight with a wireless connection, can be leased for an initial fee of US$299. Photo by Bloomberg

A filepic of second-hand luxury handbags being displayed at a Milan Station outlet in Hong Kong. Since launching the service a year ago, Entrupy says its accuracy has improved to better than 98% for 11 brands including Louis Vuitton, Chanel and Gucci. Photo by Reuters

A filepic of a fake LVMH handbag (right) purchased and shipped from a China-based online website being shown next to products on display at a Louis Vuitton store in Maryland, the US. Apparel makers will spend US$6.15 billion on anti-counterfeit technologies in 2017, according to London-based researcher Visiongain, but the anonymity of Internet shopping and the growing popularity of second-hand dealers are making the war against fakes harder. Photo by Reuters

Srinivasan: The technology works pretty well on everything except for diamonds and porcelain, because those are refractive and we use optical analysis. Photo by Bloomberg

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Distinguishing an authentic Louis Vuitton bag from a well-made fake is a subtle art that involves counting stitches, feeling the leather’s grain and poring over print patterns. A New York start-up says it has a technology that can spot counterfeits without the guesswork.

Entrupy’s solution is a handheld microscope camera that lets anyone with a smartphone check a luxury accessory within minutes. Since launching the service a year ago, the company says its accuracy has improved to better than 98% for 11 brands including Louis Vuitton, Chanel and Gucci.

Holographic tags, microprinting and even radio beacons woven into fabric have been used by fashion labels for years to help establish the authenticity of their products. Apparel makers will spend US$6.15 billion (RM25.77 billion) on anti-counterfeit technologies in 2017, according to London-based researcher Visiongain, but the anonymity of Internet shopping and the growing popularity of second-hand dealers are making the war against fakes harder.

“Even 10 years ago, a woman going to buy a second-hand bag would know very well that Chanel, Gucci and Prada don’t sell on the street corner,” says Susan Scafidi, director of the Fashion Law Institute at Fordham University in New York. “But now, with so much legitimate and illegitimate commerce occurring online, it is very difficult for consumers to tell the difference.”

The issue was highlighted last year when the International AntiCounterfeiting Coalition suspended the membership of China’s biggest online retailer, Alibaba Group Holding Ltd, amid criticism that it and other e-commerce marketplaces weren’t doing enough to cull fakes. Alibaba founder Jack Ma didn’t help matters when he said that Chinese-made knock-offs today can offer better quality than the genuine articles.

Second-hand online stores such as RealReal and Vestiaire Collective use experts with years of experience to determine the authenticity of the goods they buy and sell. It’s a painstaking process that isn’t absolutely foolproof, according to some online reviews from customers who complain they’ve been sold counterfeits.

Entrupy says its camera magnifies objects 260 times, so features invisible to the human eye become telltale signs: misshapen stamp marks, tiny gaps in leather grain, and paint overruns.

The device, which looks like a bulky flashlight with a wireless connection, can be leased for an initial fee of US$299. Monthly plans start from US$99. So far, about 160 businesses including pawnshops, wholesalers, and online retailers have signed up.

“Today everything is done by humans,” Entrupy co-founder Vidyuth Srinivasan says by telephone. “For businesses that are growing, that’s not a scalable solution.”

Srinivasan and two New York University researchers, Ashlesh Sharma and Lakshminarayanan Subramanian, started Entrupy in 2012, a year that was a turning point for computer vision.

A breakthrough in algorithms at a science competition called ImageNet vastly improved the ability of machines to identify everyday objects in photographs by using massive data sets to find patterns. It was a watershed moment for deep-learning technologies that also underpin self-driving cars and better speech-recognition software.

With some help from Yann LeCun, Facebook Inc’s director of artificial intelligence research and an angel investor in Entrupy, Srinivasan and his partners started with a hunch that computers could be trained to look at pictures of luxury goods and extract a kind of genome, an essence of, say, a Fendi or a Hermes handbag.

The problem was that deep-learning requires tons of data they didn’t have: None of the founders had a closet full of designer handbags, fake or otherwise.

After some unfruitful spy missions to the women’s sections of department stores, they convinced several New York second-hand shops to give them access to their inventories. Getting the knock-offs was easier: One of the co-founders brought a suitcaseful back from a trip to China. Entrupy’s database now has tens of millions of photographs from about 30,000 different handbags and wallets. The software learns as clients upload new pictures.

Srinivasan says the company has no relationships with any of the fashion brands whose products they authenticate. LVMH Moët Hennessy Louis Vuitton Se and other makers of luxury goods prefer not to acknowledge that there is a second-hand market for their merchandise.

Entrupy in July raised US$2.6 million from investors led by a venture between Tokyo-based Digital Garage Inc and Daiwa Securities Group Inc. The money will be used to design a faster and more portable camera and add more brands to Entrupy’s list, according to Srinivasan, who says the company is also looking at other uses for its software.

“The technology works pretty well on everything except for diamonds and porcelain, because those are refractive and we use optical analysis,” Srinivasan says. “We’ve already tested it on auto parts, phones, chargers, headphones, jackets, shoes, even crude oil.” — Bloomberg