RFinger: Environmental Fingerprint Embedding for Harmless mmWave Dataset Ownership Verification

The rapid evolution of millimeter-wave radar sensing technology has given rise to a proliferation of open-source radar datasets, creating an urgent need for innovative digital copyright protection techniques. However, conventional image and audio watermarking techniques are inadequate for radar copyright protection due to radar signals' sparsity, vulnerability and complexity. In this paper, we present RFinger, an ownership verification framework for static indoor millimeter-wave radar datasets. Our approach encodes environmental information extracted from radar signals into digital watermarks, strategically embedding these within carefully selected data frames to establish robust verification credentials. We develop statistical hypothesis testing metrics to detect unauthorized access to RFinger-protected data in black-box setting. Our strategic watermark design ensures that the unauthorized models exhibit distinctly anomalous performance on verification data compared to legitimate models. Through experiments on two large millimeter-wave radar datasets, we have validated that our designed strategy provides high watermark retrieval accuracy without compromising downstream tasks.