Pastel’s Unrivaled Dupe-Detection System (Part 1)
In this 2-part post, we lay out Pastel Network’s “dupe detection” system — arguably the single most innovative part of our software. Before we jump into the catalyst for such a feature and dive into its inner workings, we thought a quick description of it would be appropriate.
Pastel’s novel Near-Duplicate Detection leverages 7 deep-learning models using Tensorflow with Keras Applications and transforms each NFT file into a fixed list of ~8,000 numbers (the NFT ‘fingerprint’). We assess the correlation between a given fingerprint and the database of all existing fingerprints on Pastel, competitive NFT platforms like OpenSea, and even open databases of images on the entire internet (e.g. Google). Finally, we assign the NFT a level of rareness (e.g. ‘Rare to Pastel’ or ‘Highly Rare’).
Part 1 — The Need
The need for a “dupe detector” is fairly obvious, but no other player seems to view it as essential to their platform. For example, when you buy an NFT on any other layer-2 marketplace (Nifty Gateway, OpenSea, Rarible) you are just buying any particular file uploaded by the creator itself — with no concern for the authenticity or originality of the data. A majority of marketplaces are simply built on other infrastructure like Ethereum, and none are able to boast an entirely purpose-built, native blockchain. As a result, they cannot incorporate the bespoke technical architecture and software required to determine whether a digital asset is truly a duplicate or not.
This seems counterintuitive as the whole idea of NFTs hinges on the assurance that the intrinsic rareness continues and cannot be subverted without undermining the creator’s entire value proposition.
Of course, one might suggest: “Who cares if someone else who is not the creator registers a similar NFT? It’s up to the collector to determine if a given creation is genuine.” Well, that won’t work for us.
We believe that such a stance is counter-productive, since it deflects the problem onto users and exposes both creators and collectors to a variety of scams. Thus, in theory, near dupe detection is a no-brainer; however, in practice, duplicate image detection is, perhaps, the most technically challenging feature required in any decentralized NFT registry.
The idea is simple: if an authentic, original image is registered on the network, the platform should be able to assess and validate the level of ‘rareness.’ The standard way many crypto projects characterize NFTs is by taking the hash of the file as the ID or fingerprint of the underlying data. The problem with this method is that someone can then simply modify or outright copy the NFT and re-upload it to the system. This would cause the hash to completely change, even though the file is similar.
By characterizing the intrinsic content of an NFT into a far more robust fingerprint, our system can react similarly to the way a human observer could in determining if two images are “related.” For example, if an average person could reliably determine that a given image is similar to another, then we want our automated system to reach the same conclusion.
Collectors may prefer to spend more on an NFT that is rare at the level of the pixel patterns. Other users that are insensitive to the rarity of the NFT and view it as more of a digital collectable or as a way to support their favorite creators can buy the NFTs that are signed by the artist, even if they aren’t “rare” in this stricter sense.
Pastel has developed a novel and innovative near-duplicate detection system, which leverages advances in machine learning technology as well as the creative application of classical statistical techniques.
Check out Part 2 to see how our founders and team of world-class developers built it.
About Pastel
Pastel is the world’s first fully dedicated, decentralized NFT platform allowing users to register, store, and trade ‘provably rare’ assets. The platform gives creators a way to securely connect with fans and sell unique, limited edition digital assets –without the high fees or storage constraints of other crypto projects. Pastel also allows for the development of third-party applications to sit on top of the Network, enabling developers to enjoy the scalable registration features, storage processes, and security of the broader network. By leveraging a purpose-built native blockchain, Pastel deploys a number of key features such as a deep-learning based duplicate detection system to ensure proven authenticity.
The network is a fork from Z-cash, running the Proof-of-Work (PoW) algorithm Equihash and also supports Supernodes, which provide computational resources to the network to support asset registration, distribution, and permanent storage. Pastel is managed by world-class developers, cryptographers, and technologists, supported alongside an experienced and extensive network of marketers, influencers, and third-party agencies. Pastel is backed by key stakeholders including Innovating Capital, a prominent venture fund.
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