Jump out of Resonance: A Practical NFC Tag Fingerprinting Scheme

NFC tag authentication is crucial for preventing tag misuse. Existing NFC fingerprinting methods use physical-layer signals, which incorporate tag hardware imperfections, for authentication purposes. However, these methods suffer from limitations such as low scalability for a large number of tags or incompatibility with various NFC protocols, hindering practical application. To address these issues, we propose a new NFC fingerprinting scheme called NFChain $^+$ . Instead of sticking to the NFC resonant frequency, NFChain $^+$ excavates the tag hardware uniqueness from the protocol-agnostic tag response signal using an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies. This significantly improves fingerprint scalability. However, extracting the desired fingerprint presents two challenges: fingerprint inconsistency under different configurations, and fingerprint variations due to the signal noise in generic readers. To overcome these challenges, we design an effective signal elimination method to remove the effect of device configurations and employ contrastive learning to reduce fingerprint variations for accurate tag authentication. We further cultivate a data augmentation strategy to save the cost of manually collecting fingerprint measurements for training the authentication model. Extensive experiments show that we can achieve as low as 3.4% FRR and 4.1% FAR for over 600 NFC tags.