Active Motor Noise Cancellation (AMNC) ships in commercial fused deposition modeling (FDM) 3D printers as a hardware countermeasure against acoustic side-channel attacks that target intellectual property (IP). We present the first empirical evaluation of a deployed AMNC countermeasure, using a public dataset of synchronized acoustic and vibration recordings from two AMNC-equipped Bambu Lab printers across 12 object classes. AMNC fully neutralizes the acoustic channel: classification accuracy is indistinguishable from the 8.33% random baseline. The vibration channel, which AMNC does not target, still leaks. With summary statistics the leak is coarse and amplitude-driven (vibration accuracy approximately 31% pooled, 36-47% within-printer), while the waveform shape carries essentially nothing (frequency-only features at chance). A full-sequence temporal model that ingests the ordered evolution of the print raises accuracy to approximately 61%, and an order-shuffling control (approximately 33%) shows that a substantial component is genuinely sequential and tied to print progression. The leak is device-specific: a classifier trained on one printer transfers near chance to the other. We conclude that AMNC is an acoustic-only defense: vibration remains a partial, geometry-correlated side channel it does not address, but one that does not, on this dataset, support full geometric reconstruction; reconstruction-grade attacks would require the magnetic or power channels AMNC also leaves untouched. We release all code.

翻译：主动电机降噪（AMNC）作为硬件对抗措施，已部署在商用熔融沉积成型（FDM）3D打印机中，用于防御针对知识产权（IP）的声学侧信道攻击。我们首次对已部署的AMNC对抗措施进行实证评估，采用来自两台配备AMNC的Bambu Lab打印机在12个物体类别上的同步声学与振动记录公开数据集。AMNC完全中和了声学信道：分类准确率与8.33%的随机基线无显著差异。未被AMNC针对的振动信道仍存在泄漏。基于汇总统计，该泄漏为粗粒度且由振幅驱动（振动准确率约31%的混合集，36-47%的机内集），而波形形状几乎不携带信息（仅频率特征为随机猜测）。摄入打印有序演化序列的全时序模型将准确率提升至约61%，而顺序打乱的对照实验（约33%）表明，泄漏的实质成分具有序列性且与打印进程相关。该泄漏具有设备特异性：在一台打印机上训练的分类器在另一台上接近随机猜测。我们得出结论：AMNC仅为声学防御；振动仍是一个未解决的、与几何结构部分相关的部分侧信道，但基于该数据集尚不支持完整几何重构；重构级攻击需依赖于AMNC同样未处理的磁场或电源信道。我们已公开全部代码。