3D打印噪声抑制仍难逃侧信道攻击，打印时长成关键泄露源。
Side-Channel Attacks Survive Noise Cancellation in 3D Printers
- 通过声学与振动数据对比，发现噪声抑制仅削弱但未消除侧信道泄漏。
- 60秒窗口下分类准确率达40.28%，打印时长单独可判别63.89%。
- 普通手机麦克风即可捕获泄露信息，适合关注设备安全的研究者。
主动电机噪声抑制（AMNC）是商用熔融沉积建模（FDM）3D打印机的降噪功能。它虽压制了侧信道攻击依赖的声学信号，但无证据表明其设计初衷为安全控制。本文基于两个Bambu Lab打印机的144组同步声学与振动记录（12类物体），评估了配备AMNC硬件的侧信道泄漏情况。谱分析显示噪声抑制有效：电机共振频段仅比背景基线高4.92 dB。然而泄漏依然存在：30秒窗口分类准确率为11.11%（随机水平），60秒窗口达27.08%，分布式采样下升至40.28%（基线8.33%）。主导特征为打印时长本身，仅凭录音长度可实现63.89%分类（95%置信区间[55.78, 71.28]），与切片G-code时间相关系数r=0.907。振动数据携带独立于时长的几何信息：等长截断后分类率达45.83%（基线25%，置换检验p=0.023），但在配对比较中未提供额外增益。该泄漏具有架构特异性：核心-XY设备泄漏显著（36.11%），床式旋转设备接近随机（13.89%）。消费级手机麦克风性能可达固定加速度计（25.00% vs 26.39%）。噪声抑制延长了攻击所需观察时间，但未消除通道，且完全无视打印时长。
原文摘要 · Abstract (English)
Active Motor Noise Cancellation (AMNC) is a noise-reduction feature shipped in commercial fused deposition modeling (FDM) 3D printers. Because it suppresses the acoustic emissions that side-channel attacks exploit, it has security-relevant side effects, though we find no evidence it was designed as a security control. We present a duration-controlled evaluation of side-channel leakage on AMNC-equipped hardware, using a public dataset of 144 synchronized acoustic and vibration recordings from two Bambu Lab printers across 12 object classes. Spectral analysis confirms suppression is measurably active: the motor-resonance band rises only 4.92 dB above its background-relative baseline during printing. Leakage nonetheless survives it. Acoustic classification is at chance for 30-second windows (11.11%, permutation p = 0.188) but reaches 27.08% on a duration-clean 60-second window and 40.28% under distributed sampling, against an 8.33% baseline: the channel is suppressed within short windows, not eliminated. The dominant discriminator, however, is print duration itself, which classifies at 63.89% (95% CI [55.78, 71.28]) from recording length alone and is validated against sliced G-code print time at r = 0.907. Vibration carries genuine geometry information independent of duration: with the observation window equalized by truncation, classification reaches 45.83% against a 25% baseline (permutation p = 0.023). It nonetheless adds no measurable information beyond duration in paired comparison. The leak is architecture-specific, present on the core-XY device (36.11%) and indistinguishable from chance on the bed-slinger (13.89%), and a consumer handset recovers as much as a mounted accelerometer (25.00% vs 26.39%). Noise cancellation raises the observation time an acoustic attacker requires without eliminating the channel, and leaves print duration entirely untouched.
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