New York (ISJ) - A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of consumer products.
"The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products—corresponding to the same larger product line—exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions," explained New York University Professor Lakshminarayanan Subramanian.
The system is commercialized by Entrupy Inc., an NYU startup founded by Ashlesh Sharma, a doctoral graduate from the Courant Institute, Vidyuth Srinivasan, and Subramanian.
Counterfeit goods represent a massive worldwide problem with nearly every high-valued physical object or product directly affected by this issue, the researchers note. Some reports indicate counterfeit trafficking represents 7 percent of the world's trade today.
While there are several other counterfeit-detection methods, these are invasive and run the risk of damaging the products under examination.
The Entrupy method, by contrast, provides a non-intrusive solution to easily distinguish authentic versions of the product produced by the original manufacturer and fake versions of the product produced by counterfeiters.
It uses a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes.
"The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects," claimed Subramanian.
Source: New York University
Image courtesy: New York University/Entrupy