当时间扰动如同传感器偏置：可穿戴活动识别器的无标签审计

中文摘要 AI 辅助

可穿戴人体活动识别（HAR）模型在传感器、受试者和骨干网络上运行，然而一个平滑的波形可能看似时间性的，却主要利用了持续的传感器偏移。我们提出了SpectrumAudit，一种标签密封的审计方法，它在从训练和测试中排除的受试者的校准窗口上拟合一个相位随机化的全窗口刺激。选择后，它在同一冻结受害者上重放其精确的直流投影和预算受限的零均值残差，无需重新拟合。在来自三个数据集和三个骨干网络的27个受害者中，所选波形导致2.87-40.83个百分点的三相鲁棒性准确率损失。在此重放预算下，直流在24/27个受害者上比交流更具破坏性，并在22/27个受害者上恢复了至少90%的完整下降；所有5个失败均发生在WISDM上。在保留的UTD-MHAD检查中，所选波形导致13.47个百分点的准确率和11.68个百分点的宏F1损失，而匹配的随机变化为-0.66个百分点。该审计在常见峰值预算上限下诊断偏移与零均值变化。代码将在接受后发布。

英文摘要

Wearable human-activity recognition (HAR) models operate across sensors, subjects, and backbones, yet a smooth waveform may appear temporal while exploiting a persistent sensor offset primarily. We introduce SpectrumAudit, a label-sealed audit that fits a phase-randomized full-window stimulus on calibration windows from subjects held out from training and testing. After selection, it replays its exact DC projection and budget-constrained zero-mean residual on the same frozen victim without refitting. Across 27 victims from three datasets and three backbones, the selected waveforms cause 2.87-40.83-point three-phase robust accuracy losses. Under this replay budget, DC is more damaging than AC on 24/27 victims and recovers at least 90% of the full drop on 22/27; all 5 failures occur on WISDM. In a held-out UTD-MHAD check, the selected waveform causes 13.49-pp accuracy and 11.68-pp macro-F1 losses, versus -0.66 pp for matched random changes. The audit diagnoses offset versus zero-mean variation under a common peak-budget cap. The code will be released upon acceptance.

发表机构

• Defense Innovation Institute, Academy of Military Science（军事科学院国防科技创新研究院）
• School of Software Engineering, South China University of Technology（华南理工大学软件学院）

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