NFChain: A Practical Fingerprinting Scheme for NFC Tag Authentication

Abstract

NFC tag authentication is highly demanded to avoid tag abuse. Recent fingerprinting methods employ the physicallayer signal, which embeds the tag hardware imperfections for authentication. However, existing NFC fingerprinting methods suffer from either low scalability for a large number of tags or incompatibility with NFC protocols, impeding the practical application of NFC authentication systems. To fill this gap, we propose NFChain, a new NFC fingerprinting scheme that excavates the tag hardware uniqueness from the protocol-agnostic tag response signal. Specifically, we harness an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies, which significantly improves fingerprint scalability. However, extracting the desired fingerprint encounters two practical challenges: (1) fingerprint inconsistency under different NFC reader and tag configurations and (2) fingerprint variations across multiple measurements of the same tag due to the signal noise in generic readers. To tackle these challenges, we first design an effective nulling method to eliminate the effect of device configurations. Second, we employ contrastive learning to reduce fingerprint variations for accurate authentication. Extensive experiments show we can achieve as low as 3.7% FRR and 4.1% FAR for over 600 tags.