Imagine a machine that can learn algorithms based on microscopic images of two physical products to determine which is genuine and which is fake. This would be quite a breakthrough! The great news is, this technology is now a reality as we introduce this new system stemming from the premise that each object has microscopic characteristics which distinguish a specific product or product line from fake or counterfeit versions.
This system we created works through a wide-angle microscopy device that is also compatible with mobile devices; hence allowing the user to capture a product image using a phone conveniently, wherein the program gathers the pertinent microscopic details to reference with a database resulting in a faster, and more reliable authentication process.
Through this system, we can show that a machine learning algorithm like ConvNets and bag of words can help concerned individuals design an accurate classification engine which will efficiently discern and separate genuine products from fake ones. The advantage of this system is the capability to identify even high-end counterfeits which are not easily perceived by the untrained eye.
We further relate a comprehensive design for an authentication system using mobile phones and devices, portable hardware, and a verification system based on the cloud. Data evaluation uses 3 million stored images referencing various elements such as fabrics, shoes, electronics, pills, and toys. The system boasts of an accuracy rate of over 98% with the added convenience of using a mobile phone to access the system and authenticate products.
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