Physical-layer specific emitter identification (SEI) faces significant misclassification challenges of unknown devices, especially in closed-set environments and the scarcity of available labeled data. To address these limitations, we introduce a Few-Spot Self-Supervised Adversarial Augmentation Specific Emitter Identification (FS-SA2SEI) framework that integrates adversarial augmentation, and open-set recognition (OSR) mechanisms. FS-SA2SEI enhances spectral and multi-resolution feature extraction while enabling robust classification of known emitters and rejection of unknown ones. Evaluated on Wi-Fi and publicly available datasets (ADS-B, FIT/CorteXlab), the framework achieves a closed-set accuracy of 89.9%, surpassing prior benchmarks by >5%, and an open-set detection rate (OSDR) of 64.0% at a worst-case openness of 0.6. These results highlight FS-SA2SEI’s potential as a scalable solution for real-time SEI in resource-constrained IoT systems, where adaptability to dynamic environments and unseen devices is critical.