Fakes are getting to be so good you cannot differentiate the genuine one from the counterfeit. The top brands are top targets for counterfeiting, and so are some lesser-known ones. The European Union Intellectual Property Office estimates that international trade in such counterfeits is worth $509 billion or 3.3% of total sales.
The situation today is that counterfeiters have become savvy enough to use artificial intelligence and machine learning, even going to the extent of approaching companies that detect counterfeit goods in order to reverse engineer their technology. That makes it all the more imperative for brands and even public institutions to focus on more implementation of artificial intelligence to detect fakes.
Roping in the best Artificial Intelligence development companyis a good starting point to use AI to detect counterfeits in different ways. Here are the different ways AI detects counterfeits.
Role of AI in Detecting Anomalies or Inconsistencies
It would take a much-trained eye and knowledge to distinguish between a fake and an authentic product. However, AI, with machine learning, can analyze hundreds of thousands of images and detect the minutest of inconsistencies and anomalies in shape, color, texture, size, and angles.
This precision and accuracy are based on the records of a vast number of images and data of the genuine product. The machine algorithm and convolutional neural network can access the database and compare the sample under test to detect the tiniest anomalies.
However, you can get your own counterfeit, detecting technology by engaging AI development services to customize detection based on your product parameters. Apart from visual checks, there are many other ways to identify counterfeit products.
One drawback to using AI and computer vision technologies is that a recently launched product not in the counterfeit detection software database would be difficult to identify. Companies have to be cautious to incorporate data of such newly released products immediately so that the fake detection system has its parameters in its database and can compare the dataset to the fake product dataset.
Yet another drawback to optical check is that it relies on parameters such as shape, dimensions, and color, which can be matched by counterfeiters. However, future use of ultrasound, UV, and other technologies could also check for material properties. Those making fake products invariably use cheaper or different materials, and such materials will have different resonant frequencies and physical characteristics that can be determined with non-destructive tests.
AI and Blockchain
The sale and use of fake handbags and apparel can cause loss of money and reputation. But, the damages are severe with fake food products and pharmaceuticals.
So, IBM goes a step further in fraud detection by blending blockchain with imaging and artificial intelligence to verify the authenticity of products. Its Crypto Anchor Verifier makes use of machine learning, neural networks, and video analytics to evaluate properties of solids of liquids, identify the color, saturation, viscosity, and other chemical properties to identify whether the product is fake or genuine. Its software relies on a ton of data captured from genuine products, and the software runs on smartphones, making it inexpensive for businesses and individual users.
The blockchain part comes in, provided the manufacturers make use of it to track the movement of products from the point of origin to destination. This assumes significance when considering that a genuine product may be manufactured in one location, whereas a fake is manufactured in another location. There may be no records of the origins of such fakes.
Tracing product movement is another area where AI and machine learning can be put to use. Of course, this works only when products are traceable with paper or electronic trail.
One innovative startup company Neurotags adopted a different hardware-AI software solution to assure buyers that the product they are buying is genuine. It makes use of an open tag on the product and a hidden tag.
Buyers access the hidden tag and scan it to receive information on authenticity. If it is not genuine, the product can be returned to the store. This solution simply identifies the product but cannot strictly be termed as a solution to detect fakes.
With the rise of IoT, sensors, and AI, developers in the AI/ML space can do more than just visual checks for counterfeit detection.
The Online Retail Path
Ecommerce sites are becoming popular platforms for sales of counterfeits. A buyer only knows that the product is fake after receipt, and then they may or may not get a remedy.
Some ecommerce service providers are trying to find innovative solutions to curb the sale of counterfeit products. Amazon, for instance, is tackling the issue (which affects its reputation) by putting in place machine learning and brand registry to weed out fakes and fake sellers.
Hundreds of smaller ecommerce marketplaces would benefit by engaging expert AI development servicesto help them to spot fakes and thus ensure buyer trust as well as their reputation. DataWeave is one such company that makes use of advanced GPUs with TensorFlow cores and deep learning systems to design neural networks that analyze catalog pictures and detect discrepancies.
It follows that when people buy fakes from online retail stores, they will certainly leave feedback and complaints. This is a rich area to mine. The problem is diversity and depth of data that would be tough to handle in the usual way.
Here again, AI development services can tailor-make programs and use algorithms to capture specifically flagged data from reviews and links with suppliers and brands. Alibaba and 20 others created the Big Data Anti-Counterfeiting Alliance and closed down 230000 IP infringing stores.
Fakes cause immense losses to brand owners. Apart from monetary loss, there is damage to their prestige, and it affects future sales. Whether yours is a well-established brand or an emerging one, it pays to get a fake-products detection system developed by the top AI development company. Consider it an investment that will be recouped many times over.