What started in 2012 as a way to authenticate art has now progressed into the only technology-based, powered by AI, authentication solution on the market to provide trust to buyers and sellers of luxury handbags. Entrupy has collected thousands of images of physical goods (bags, shoes, watches, jewelry, etc) from around the world, creating a rich and diverse unmatched data-set.

We utilize this high quality data collected to teach our algorithms to differentiate between fake and authentic items, which in turn, gives us the ability to produce results like our 98.5% accuracy rate. Every item that’s authenticated helps the algorithms learn and improve, creating a smarter solution that adapts to the changing world.


Where and how is the original data collected?
Our proprietary data has been collected over years and from diverse sources from around the world. We also have data partnerships with prominent businesses to collect data on their inventory, thus assuring us pre-checked items.

How do you know that your original dataset is correct?
We run 3 different layers (2 manual verifications and one algorithmic) of filtering that allows us to correct any possible errors in the data submitted. This ensures that the algorithms are always fed extremely high quality information during the machine-training process.

Does this cover only recently available items and how do you keep updated with the new releases?

In general, we cover most styles of a particular brand or type of material. We have data samples from over 80 years ago all the way up to the recent months. This allows us to cover a wide time-range for most supported brands. In order to provide our customers with seamless authentication ability, we keep track of the recent releases and try to incorporate them as soon as possible.



To learn more about our technology, the thought process behind the building of our technology and a sneak peek into our capabilities, below is an excerpt of the paper published by the founders of Entrupy.

Entrupy published on KDD.org

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)

“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.

To download and read the complete paper, please click here.