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nft-scam-detector
Advanced tools
A lightweight detector scam classifier for NFTs with 95% accuracy. Can run anywhere that webassembly runs: on a server, in a lambda function, and even running entirely in your browser.
A lightweight detector scam classifier for NFTs with 95% accuracy. Can run anywhere that webassembly runs: on a server, in a lambda function, and even running entirely in your browser.
Also included is the model training code and data, so you can train and bring your own model if the default model is not performing well.
Feature extraction is done with a combination of on-chain data and OCR using the tesseract.js
library. Classification is done with naive bayes and hand-picked labels.
We labeled NFT into 2 categories:
npm install nft-scam-detector --save
yarn add nft-scam-detector
import { extractAndClassify } from "nft-scam-detector";
const imageUrl = "YOUR_NFT_IMAGE_URL";
const result = extractAndClassify(imageUrl);
result.then(console.log);
// result here
//
// {
// "classification": "scam",
// "scam_likelihood": 0.0001565123,
// "ham_likelihood": 0.0000004214,
// }
//
Live testing at: https://sui-nft-spam-fe.getnimbus.io/
You can test the accuracy of a model using the code in the /test
folder. Make sure that your training set and test set do not overlap. It should spit out a confusion matrix as well as all of the mistakes made with accuracy more than 95%
scam (predicted) | ham (predicted) | |
---|---|---|
scam (actual) | 20 | 0 |
ham (actual) | 2 | 18 |
MIT License
FAQs
A lightweight detector scam classifier for NFTs with 95% accuracy. Can run anywhere that webassembly runs: on a server, in a lambda function, and even running entirely in your browser.
The npm package nft-scam-detector receives a total of 49 weekly downloads. As such, nft-scam-detector popularity was classified as not popular.
We found that nft-scam-detector demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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