iBA: Backdoor Attack on 3D Point Cloud via Reconstructing Itself

TL;DR

This paper introduces MirrorAttack, a novel 3D backdoor attack method that reconstructs clean point clouds to embed imperceptible triggers, effectively bypassing defenses and achieving high attack success rates.

Contribution

The paper proposes MirrorAttack, a data-driven 3D backdoor attack that uses auto-encoder reconstruction to create imperceptible and robust triggers without manual design.

Findings

Achieves state-of-the-art attack success rates against various models.

Triggers are highly imperceptible and resistant to preprocessing defenses.

Effective even with minimal perturbations, ensuring stealth.

Abstract

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.