  The widespread deployment of Deep Neural Networks (DNNs) for 3D point cloud
processing starkly contrasts with their susceptibility to security breaches,
notably backdoor attacks. These attacks hijack DNNs during training, embedding
triggers in the data that, once activated, cause the network to make
predetermined errors while maintaining normal performance on unaltered data.
This vulnerability poses significant risks, especially given the insufficient
research on robust defense mechanisms for 3D point cloud networks against such
sophisticated threats. Existing attacks either struggle to resist basic point
cloud pre-processing methods, or rely on delicate manual design. Exploring
simple, effective, imperceptible, and difficult-to-defend triggers in 3D point
clouds is still challenging.To address these challenges, we introduce
MirrorAttack, a novel effective 3D backdoor attack method, which implants the
trigger by simply reconstructing a clean point cloud with an auto-encoder. The
data-driven nature of the MirrorAttack obviates the need for complex manual
design. Minimizing the reconstruction loss automatically improves
imperceptibility. Simultaneously, the reconstruction network endows the trigger
with pronounced nonlinearity and sample specificity, rendering traditional
preprocessing techniques ineffective in eliminating it. A trigger smoothing
module based on spherical harmonic transformation is also attached to regulate
the intensity of the attack.Both quantitive and qualitative results verify the
effectiveness of our method. We achieve state-of-the-art ASR on different types
of victim models with the intervention of defensive techniques. Moreover, the
minimal perturbation introduced by our trigger, as assessed by various metrics,
attests to the method's stealth, ensuring its imperceptibility.
