AI 中文总结
针对硬件替换导致物理实现改变而逻辑身份不变的问题，提出FreqSpaNet网络，利用SFPF的频率与空间特征进行开放集异常检测，平均AUROC达96.31%，优于基线9.05个百分点。
AI 中文摘要
未经授权的硬件替换可以在保留无线设备逻辑身份的同时改变其物理实现，这对硬件完整性验证构成了挑战。空频极化指纹（SFPF）捕获了设备在多个频率和方向上的依赖响应，但其频率和空间维度表现出不同的结构依赖性。我们提出了FreqSpaNet，一种用于开放集硬件异常检测的SFPF表示学习网络。频率分支捕获相邻频率间的局部变化，而几何感知的空间分支利用角度信息建模方向关系。通过自适应融合将两种表示结合，互补预训练进一步捕获共享信息，同时保留频率和空间表示的独特特征。实验表明，FreqSpaNet的平均AUROC达到96.31%，比基线高出9.05个百分点。在七种硬件替换场景下的结果进一步验证了FreqSpaNet的有效性。
英文摘要
Unauthorized hardware replacement can preserve a wireless device's logical identity while altering its physical implementation, posing a challenge to hardware integrity verification. Spatio-frequency polarization fingerprints (SFPFs) capture device-dependent responses across multiple frequencies and directions, but their frequency and spatial dimensions exhibit different structural dependencies. We propose FreqSpaNet, an SFPF representation learning network for open set hardware anomaly detection. A frequency branch captures local variations among neighboring frequencies, while a geometry-aware spatial branch models directional relationships using angular information. The two representations are combined through adaptive fusion, and complementary pretraining further captures shared information while preserving the distinct characteristics of the frequency and spatial representations. Experiments show that FreqSpaNet achieves a mean AUROC of 96.31\%, 9.05 points above the baseline. Results under seven hardware replacement scenarios further verify the effectiveness of FreqSpaNet.
Comments5 pages, 6 figures
