Use of AI Against Counterfeiting

November 2, 2019

Artificial intelligence

The expression “Artificial Intelligence” (AI) is stacked with a lot of suppositions taken from many years of sci-fi motion movies about robots taking control over the world. A long way from being a part of a whimsical anecdote about your microwave creating emotions however, AI is as of now a common truth of modern life. Despite the fact that fantasies of robots who can have our spot at work while we go through our days golfing and going for long snacks might be some distance later on, what computer scientists call “narrow” or “weak” AI is as of now integrated into our everyday lives.

Progressively, the issues presented by counterfeiting are on the web. The size of the issue fluctuates by brand, nation and platform, however, plainly a few brands and products are being disproportionately affected by counterfeit online deals such that they wouldn’t be by physical shops or merchants. Right off the bat in 2018, the US games organization Asmodee evaluated that for some of the games they sell, around 70% of US deals are counterfeit. Obviously not all brands are influenced in such an emotional way, however, it demonstrates the earnestness of the online issue.

In 2017, US Customs held onto more than 34,000 shipments of counterfeit products, an 8% increase from 2016. Retailers are scrambling to stretch out beyond the forgers and have started to explore how innovation and technology can help.

Artificial intelligence has expanded the arsenal of devices available to them, however, AI can be expensive to execute. In any case, the demand is growing, and a couple of organizations have quit depending exclusively on human aptitude. As fakes get all the more persuading, they’ll in the end, arrive at a sort of peculiarity, where human specialists will battle to differentiate between the genuine and the fake.

As of now, organizations are offering sophisticated solutions for companies and brands. Entrupy, just as Red Points, Cypheme and different organizations, represents considerable authority in the low-cost high-volume ID of fake items. These organizations offer innovations that examine materials, hues, packaging and different attributes to spot fakes.

IBM Research has created something many refer to as Crypto Anchor Verifier, which is an AI counterfeit detector that utilizes blockchain and runs on a cell phone. To utilize it, you take a snap photo of any item, and the application at that point runs a comparison of that picture against a database in a blockchain ledger to decide credibility.

Shockingly, in any case, the requirement for AI solutions may really be progressively articulated for those brands who as of now have existing brand protection procedures and an increasingly full-grown anti-counterfeiting program set up. For brands who have just invested time taking out an anti-counterfeiting program, there will be an effectively recognizable change in the manner fakes are depicted.

To take a particular example, for football clubs one thing that regularly happens is that dealers of fakes rapidly understand that their listings are expelled when they incorporate a team name in the description of the product. Thus, the listings are reintroduced with a marginally extraordinary description utilizing more conventional terms, for example, “Spanish football crew kits 2018/2019,” for instance.

This makes these listings harder to discover for both human and machine searches, yet despite everything they have a spot on the web and might be found by buyers. In this occurrence, one of the developing AI innovations for what’s to come is probably going to be image recognition. With image recognition, it will be a lot simpler for brands to search out and expel those listings which utilize their IP to sell fake items, in this way consolidating all the accessible technologies in the smartest time productive way that could be available.

Amazon uses machine learning, alongside programming engineers, research scientists, program managers and investigators, for its Brand Registry program, which the organization says cut encroachments by 99%. However, numerous brands aren’t a part of the library.

Amazon divulged Project Zero, which it says will enable brands to bring down fake things all alone without Amazon’s assistance. Amazon which has since quite a long battled with fake items on its site, will likewise automatically screen for counterfeit things. The project utilizes machine learning that continually checks Amazon’s stores and expels suspected fakes. Organizations give Amazon their logos, trademarks and other significant data about their brands, and Amazon checks item postings constantly searching for false things before they are bought.

Seattle-based startup DataWeave, competitive intelligence service provider for retailers and consumer brands, offers a counterfeit product detection system that utilizes deep learning on how to recognize and help dispense with counterfeit items from ecommerce sites.

Utilizing NVIDIA GeForce GTX 1080 and GeForce GTX 1080 Ti GPUs with the cuDNN-accelerated TensorFlow and Caffe deep learning systems, the team prepared their neural network on a large number of catalogue pictures of products, for example, hardware, makeup, clothing, footwear, and furniture. The organization says the fake items are spotted by “minute disparities” in catalog images and content, which the deep learning framework can rapidly recognize.

While the war over fakes will never be won, it’s feasible for companies and brands to pick up the advantage. By democratizing and automating anti-counterfeit systems, it will be conceivable to stop fakes at scale. Yet, it won’t work without the collaboration and investment of the organizations that make real items.