Copymint Prevention for NFT Marketplaces: A Case Study on OpenSea
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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