The expression "artificial intelligence" (AI) is full of assumptions from years of sci-fi movies involving the world of robotic control. So far, artificial intelligence has become a universal truth in modern life, and there is still a long way to go before it becomes a whimsical anecdote that generates emotions in the microwave.

How do we use AI to prevent forgery?
How do we use AI to prevent forgery?

Even though fantasy robots can play a role in golfing and snacking in the future, computer scientists have so far called "narrow" or "weak" artificial intelligence into our daily lives.

The problems raised by counterfeiting appeared on the Internet. It is clear that some brands and products are being disproportionately affected by counterfeit online transactions, so brick-and-mortar stores or merchants will not do so.

The American game organization Asmodee quickly evaluated it in 2018, and the assessment showed that about 70% of the US transactions sold were forged in certain games sold. Not all brands are affected in this emotional way, but this shows the sincerity of online distribution.

In 2017, US Customs detained more than 34,000 counterfeit products, an increase of 8% over 2016. Retailers are scrambling to expand beyond counterfeiters and begin to explore how innovation and technology can help.

Artificial intelligence has expanded the library of devices available to them, but the cost of implementing artificial intelligence can be high. In any case, demand is growing, and two organizations have withdrawn, depending entirely on human capabilities.

Counterfeiters become more persuasive. They will eventually reach a particularity, where human experts will fight to distinguish between real and fake goods.

So far, organizations are providing sophisticated solutions for companies and brands. Like Red Points, Cypheme, and other organizations, Entrupy represents a massive license for low-cost, high-quality fake IDs. These organizations offer innovative technology that can inspect materials, tones, packaging, and different attributes to discover fakes.

IBM Research has created several things called the Crypto Anchor Verifier, an AI forgery detector that utilizes a blockchain and runs on a cell phone. To take advantage of it, you need to take a snapshot of any project, and the app compares the image to the database in the blockchain ledger to determine the credibility.

Surprisingly, in any case, the demand for AI solutions may be gradually clarified for brands that already have existing brand protection programs and have established increasingly sophisticated anti-counterfeiting programs. For brands that have just invested time in anti-counterfeiting programs, effective identifiable changes will occur in the counterfeit depiction.

As a concrete example, one of the things that often happens to football clubs is that fake dealers quickly understand that when they add a team name to the product description, their items are expelled.

Therefore, when re-introducing the list, some more conventional terms, such as "Spanish football team kit 2018/2019", were used to make an extraordinary description on edge.

As a result, these lists are hard to find, whether it's manual search or machine search, even though they have a place on the web and may be found by buyers. In this case, one of the emerging AI innovations may be image recognition.

With image recognition, brands can more easily search for and evict goods that use their IP to sell counterfeit products; in this way, consolidating all accessible technologies most intelligently and efficiently possible.

Amazon uses machine learning and programming engineers, research scientists, project managers, and researchers with its brand registration program, which the organization says has reduced infringement by 99%. However, many brands are not part of the library.

Amazon revealed the No. 0 project, which said that the No. 0 project will enable the brand to sell fakes alone without Amazon's help. For a long time, Amazon has been fighting fake and shoddy items on its website, but it also automatically screens for counterfeit items.

The project uses machine learning to check Amazon's stores and expel suspected fakes continually. The organization provides its logo, trademarks, and other vital data about its brand to Amazon, and Amazon continuously checks for product releases before the release of the product to find false information.

Seattle-based startup DataWeave, a competitive intelligence service provider for retailers and consumer brands, offers a counterfeit product detection system that leverages deep learning about how to identify and help eliminate counterfeit products from e-commerce sites.

The team used the NVIDIA GeForce GTX 1080 and GeForce GTX 1080 Ti GPUs in conjunction with the cuDNN-accelerated TensorFlow and Caffe deep learning systems to catalog images in a wide range of products (eg, hardware, cosmetics, clothing, footwear, and furniture. Forged items are discovered by "small differences" in the catalog images and content, and the deep learning framework can quickly identify these counterfeit items.

Although it will never win a war on fakes, it is feasible for companies and brands to take advantage of this. If there is no cooperation and investment in the organization that makes the real thing, it will not work correctly.

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