Manipulation-Resilient Pricing for Nonfungible Tokens

Abstract

Non-Fungible Tokens (NFTs) enable the decentralized representation and exchange of real-world assets, supporting features like fractional ownership and programmable logic that underpin emerging digital finance ecosystems. However, the openness of NFT markets makes them susceptible to manipulation tactics like wash trading, where coordinated trades distort prices. This undermines valuation accuracy and erodes trust in decentralized finance. To counter these challenges, we propose NFTGuard, a unified framework for detecting wash trades and producing manipulation-resilient NFT price predictions. First, NFTGuard filters out manipulated transactions using a rule-based detector that identifies self-dealing and cyclic trading patterns prevalent in decentralized marketplaces. Second, NFTGuard prepares for price prediction by constructing a multi-modal representation that integrates temporal trading dynamics, transactional metadata, and asset-specific semantic signals. Third, NFTGuard performs prediction using a Multi-Layer Perceptron (MLP) mixing backbone that fuses these heterogeneous cues into manipulation-resilient forecasts. Experiments on real-world NFT datasets from platforms like Rarible and Opensea show that NFTGuard achieves a 90.9% F1-score in detecting wash trades and improves price prediction accuracy by over 10% compared to the baselines.