The future 6G systems are expected to achieve intelligent connection and interaction between various heterogeneous terminals, increasing the fragility for spoofing attacks. Due to the high security and energy efficiency, physical layer authentication (PLA) has been regarded as a powerful method to verify the identity of devices. Nevertheless, due to the inaccurate identifying fingerprints caused by the imperfect estimation and variations of the limited fingerprints, most of the state-of-the-art PLA schemes have low reliability and robustness in low-signal-noise ratio (SNR) environments. Besides, most PLA schemes rely on the prior knowledge of attackers to establish authentication models, thus reducing the feasibility of actual communications. To address the first challenge, we propose a multiattacker detection architecture based on multidimensional fingerprints, which can provide more robust identifiable spatial attributes for devices by using fingerprints observed by receivers in multilocations. Upon the designed detection architecture, to tackle the second issue, we propose four clustering-based PLA schemes without requiring their training fingerprint sets. Considering that the aforementioned schemes can divide fingerprints from different transmitters into several disjoint clusters but cannot precisely identify forged fingerprints, we further propose the graph learning-based PLA approaches with only a few labeled fingerprints. The simulation results on real industrial outdoor and indoor data sets demonstrate the superiority of the designed detection system in adjusted mutual information (AMI) and authentication accurate rate (AucRate) over the single observation-based PLA schemes.