AI Powered Authentication
Entrupy’s intelligent, network-driven solutions for product authentication are powered by artificial intelligence, with proprietary algorithms trained on a rich and diverse data set of millions of images from known-authentic and known-counterfeit products. These algorithms run security, in the cloud, ensuring complete objectivity and eliminating risks of human error and bias.
When an item is processed using our app, a set of images are collected and analyzed by these algorithms, which run securely in the Cloud. With speed and pinpoint accuracy, the algorithms either verify the item’s authenticity or return an “unverified” result within minutes and often instantly. Each new scan adds to the database in real time, further training the algorithms and making our solution smarter with every verification performed.
AI POWERED AUTHENTICATION
Entrupy’s secure, accurate and easy to use solutions are built for fast implementation and unlimited scale, require only a few components:
The solution uses 256 bit encryption, securing Edge to Cloud control and data traffic
Data and Algorithms: Foundations of Our Authentication Solution
Entrupy trains its algorithms to differentiate between fake and authentic items using an unique set of data with millions points based on known-real and known-counterfeit items. Because the solution is machine learning-driven, every item that undergoes verification helps the algorithms learn and improve, creating a smarter solution that adapts to the changing world.
Check out this paper published by the founders of Entrupy to learn more about the technology powering our authentication system.
The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue
Ashlesh Sharma (Entrupy Inc); Vidyuth Srinivasan (Entrupy Inc); Vishal Kanchan (Entrupy Inc); Lakshminarayanan Subramanian (Entrupy Inc)
Entrupy published on KDD.org
“We introduce a new mechanism that uses machine learning algorithms on microscopic images of physical objects to distinguish between genuine and counterfeit versions of the same product. 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. A key building block for our system is a wide-angle microscopy device compatible with a mobile device that enables a user to easily capture the microscopic image of a large area of a physical object. Based on the captured microscopic images, we show that using machine learning algorithms (ConvNets and bag of words), one can generate a highly accurate classication engine for separating the genuine versions of a product from the counterfeit ones; this property also holds for super-fake counterfeits observed in the marketplace that are not easily discernible from the human eye. We describe the design of an end-to-end physical authentication system leveraging mobile devices, portable hardware and a cloud-based object verication ecosystem. We evaluate our system using a large dataset of 3 million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. The classication accuracy is more than 98% and we show how our system works with a cellphone to verify the authenticity of everyday objects.