Echo: On the Limitations of Noise-Based Radio Fingerprinting Obfuscation

Radio Frequency Fingerprinting (RFF) enables passive device identification by leveraging hardware-induced signal distortions such as phase noise and frequency offset. This technique introduces relevant privacy risks, as passive observers can re-identify and track devices over time. To address these concerns, recent defenses have proposed injecting random noise to obscure fingerprint features and hinder classification. In this paper, we revisit the threat model from an adversarial standpoint and demonstrate that such noise-based obfuscation is fundamentally vulnerable. We introduce Echo, a lightweight signal-level attack that exploits the statistical independence of additive noise across repeated transmissions; by applying a combination of ensemble averaging with autoencoder-based reconstruction error analysis, Echo systematically suppresses obfuscating noise and restores persistent hardware signatures. We evaluate this approach on a public dataset of ten USRP transmitters under varying noise conditions and three adversarial training scenarios. Our results show that Echo achieves fingerprint recovery across 22 different test scenarios, obtaining a minimum Area Under the Curve (AUC) of 0.80. These findings expose a critical weakness in current obfuscation strategies and call for further research in the area.