Research & Development World

  • Home Page
  • Topics
    • Aerospace
    • Archeology
    • Automotive
    • Biotech
    • Chemistry
    • COVID-19
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Market Pulse
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
      • Software
    • Semiconductors
  • 2021 R&D 100 Award Winners
    • R&D 100 Awards
    • 2020 Winners
    • Winner Archive
  • Resources
    • Digital Issues
    • Podcasts
    • Subscribe
  • Global Funding Forecast
  • Webinars

Researchers Use Machine Learning to Spot Counterfeit Consumer Products

By New York University | August 24, 2017

A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product. Image courtesy of Entrupy, Inc.

A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product. 

The work, led by New York University Professor Lakshminarayanan Subramanian, will be presented on Mon., Aug. 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia.

“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,” explains Subramanian, a professor at NYU’s Courant Institute of Mathematical Sciences.

The system described in the presentation 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 other counterfeit-detection methods exist, 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 does so by deploying 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,” notes Subramanian.

A demo of the technology may be viewed here (courtesy of Entrupy Inc.).

To date, Entrupy, which recently received $2.6 million in funding from a team of investors, has authenticated $14 million worth of goods.

Related Articles Read More >

R&D 100 winner of the day: Floodlight Non-Targeted Analysis System
Object detection and tracking software accelerates thermal camera integration for ADAS and AV
Groundbreaking research could help paramedics save the lives of pedestrian casualties 
CEA-Leti scientist receives $3.30M grant to develop nanoscale memories inspired by insect nervous systems
2021 R&D Global Funding Forecast

Need R&D World news in a minute?

We Deliver!
R&D World Enewsletters get you caught up on all the mission critical news you need in research and development. Sign up today.
Enews Signup

R&D World Digital Issues

February 2020 issue

Browse the most current issue of R&D World and back issues in an easy to use high quality format. Clip, share and download with the leading R& magazine today.

Research & Development World
  • Subscribe to R&D World Magazine
  • Enews Sign Up
  • Contact Us
  • About Us
  • Drug Discovery & Development
  • Pharmaceutical Processing
  • 2022 Global Funding Forecast

Copyright © 2022 WTWH Media LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media
Privacy Policy | Advertising | About Us

Search R&D World

  • Home Page
  • Topics
    • Aerospace
    • Archeology
    • Automotive
    • Biotech
    • Chemistry
    • COVID-19
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Market Pulse
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
      • Software
    • Semiconductors
  • 2021 R&D 100 Award Winners
    • R&D 100 Awards
    • 2020 Winners
    • Winner Archive
  • Resources
    • Digital Issues
    • Podcasts
    • Subscribe
  • Global Funding Forecast
  • Webinars