Scale AI Case Studies Copymint Prevention for NFT Marketplaces: A Case Study on OpenSea

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	Copymint Prevention for NFT Marketplaces: A Case Study on OpenSea Scale AI

Copymint Prevention for NFT Marketplaces: A Case Study on OpenSea

Scale AI

Technology Category	Analytics & Modeling - Real Time Analytics Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries	Cement E-Commerce
Applicable Functions	Procurement
Use Cases	Fraud Detection Real-Time Location System (RTLS)
Services	System Integration
Challenge	OpenSea, the world's leading marketplace for non-fungible tokens (NFTs), was facing a significant challenge in detecting and mitigating copymints and fraud. Copymints are duplicates or imitations of popular NFTs, which can deceive users, especially those new to the world of NFTs. Trust and safety are crucial for welcoming new people into the Web3 ecosystem, and OpenSea was looking for a vendor to help advance their detection and removal capabilities. The team had already used rule-based systems to capture forms of deception, but it was a challenge to achieve the desired speed, recall, and precision needed to effectively address fraud in the marketplace. Read More
About Customer	OpenSea is the world's first and largest marketplace for non-fungible tokens (NFTs). The platform allows customers to browse, buy, sell, and mint NFTs across seven different blockchains. OpenSea is on a mission to build an open digital economy, helping the world’s creators, collectors, and collaborators own and shape their relationships directly. The company is a leader in the Web3 ecosystem and is committed to ensuring trust and safety for its users, especially those new to the world of NFTs. Read More
Solution	OpenSea partnered with Scale to implement an industry-leading solution that could identify and handle a dynamic set of deceptive NFTs. Scale Content Understanding provided data enhancement for better platform experiences by enriching, analyzing, and categorizing content. It proved robust in testing against OpenSea’s high data volume of up to 50 million items a week. Scale offered a fast turnaround models-as-a-service solution to categorize and determine whether a given NFT is a close match of another one. The solution included real-time detection through API-based solutions and full catalog scans to remove historical scams. Scale trained custom deep learning image models to represent NFTs as embeddings in a manifold, allowing similar items to be nearby and significantly different items to have a further distance from one another. This setup facilitated real-time querying and retrieval through k-Nearest Neighbors algorithms. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Platform as a Service (PaaS) - Application Development Platforms

Applicable Industries

Cement

E-Commerce

Applicable Functions

Procurement

Use Cases

Fraud Detection

Real-Time Location System (RTLS)

Services

System Integration

Challenge

OpenSea, the world's leading marketplace for non-fungible tokens (NFTs), was facing a significant challenge in detecting and mitigating copymints and fraud. Copymints are duplicates or imitations of popular NFTs, which can deceive users, especially those new to the world of NFTs. Trust and safety are crucial for welcoming new people into the Web3 ecosystem, and OpenSea was looking for a vendor to help advance their detection and removal capabilities. The team had already used rule-based systems to capture forms of deception, but it was a challenge to achieve the desired speed, recall, and precision needed to effectively address fraud in the marketplace.

About Customer

OpenSea is the world's first and largest marketplace for non-fungible tokens (NFTs). The platform allows customers to browse, buy, sell, and mint NFTs across seven different blockchains. OpenSea is on a mission to build an open digital economy, helping the world’s creators, collectors, and collaborators own and shape their relationships directly. The company is a leader in the Web3 ecosystem and is committed to ensuring trust and safety for its users, especially those new to the world of NFTs.

Solution

OpenSea partnered with Scale to implement an industry-leading solution that could identify and handle a dynamic set of deceptive NFTs. Scale Content Understanding provided data enhancement for better platform experiences by enriching, analyzing, and categorizing content. It proved robust in testing against OpenSea’s high data volume of up to 50 million items a week. Scale offered a fast turnaround models-as-a-service solution to categorize and determine whether a given NFT is a close match of another one. The solution included real-time detection through API-based solutions and full catalog scans to remove historical scams. Scale trained custom deep learning image models to represent NFTs as embeddings in a manifold, allowing similar items to be nearby and significantly different items to have a further distance from one another. This setup facilitated real-time querying and retrieval through k-Nearest Neighbors algorithms.

Impact #1	With the implementation of Scale Content Understanding, OpenSea was able to dramatically accelerate their copymint detection capabilities, detecting copyminted NFTs in real time. One of the critical measures of success for OpenSea was reducing the latency between an NFT being created on the platform to identifying bad content and taking it down. The second measure of success was the ability to handle the large volumes of data from OpenSea. By quickly detecting and removing inauthentic NFTs, OpenSea was able to improve user trust in their marketplace, contributing to a safer Web3 ecosystem.

Impact #1

With the implementation of Scale Content Understanding, OpenSea was able to dramatically accelerate their copymint detection capabilities, detecting copyminted NFTs in real time. One of the critical measures of success for OpenSea was reducing the latency between an NFT being created on the platform to identifying bad content and taking it down. The second measure of success was the ability to handle the large volumes of data from OpenSea. By quickly detecting and removing inauthentic NFTs, OpenSea was able to improve user trust in their marketplace, contributing to a safer Web3 ecosystem.

Benefit #1	Scale processes up to 50 million items a week
Benefit #2	Scale's solution has 95% average precision
Benefit #3	Real-time detection of copyminted NFTs

Benefit #1

Scale processes up to 50 million items a week

Benefit #2

Scale's solution has 95% average precision

Benefit #3

Real-time detection of copyminted NFTs

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Overview

Copymint Prevention for NFT Marketplaces: A Case Study on OpenSea

Operational Impact

Quantitative Benefit