3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models

by   Ronghui Mu, et al.

3D point cloud models are widely applied in safety-critical scenes, which delivers an urgent need to obtain more solid proofs to verify the robustness of models. Existing verification method for point cloud model is time-expensive and computationally unattainable on large networks. Additionally, they cannot handle the complete PointNet model with joint alignment network (JANet) that contains multiplication layers, which effectively boosts the performance of 3D models. This motivates us to design a more efficient and general framework to verify various architectures of point cloud models. The key challenges in verifying the large-scale complete PointNet models are addressed as dealing with the cross-non-linearity operations in the multiplication layers and the high computational complexity of high-dimensional point cloud inputs and added layers. Thus, we propose an efficient verification framework, 3DVerifier, to tackle both challenges by adopting a linear relaxation function to bound the multiplication layer and combining forward and backward propagation to compute the certified bounds of the outputs of the point cloud models. Our comprehensive experiments demonstrate that 3DVerifier outperforms existing verification algorithms for 3D models in terms of both efficiency and accuracy. Notably, our approach achieves an orders-of-magnitude improvement in verification efficiency for the large network, and the obtained certified bounds are also significantly tighter than the state-of-the-art verifiers. We release our tool 3DVerifier via https://github.com/TrustAI/3DVerifier for use by the community.


page 1

page 2

page 3

page 4


Robustness Certification for Point Cloud Models

The use of deep 3D point cloud models in safety-critical applications, s...

Unsupervised Representation Learning for Point Clouds: A Survey

Point cloud data have been widely explored due to its superior accuracy ...

PointGuard: Provably Robust 3D Point Cloud Classification

3D point cloud classification has many safety-critical applications such...

Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network

Although 3D point cloud classification neural network models have been w...

Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization

Although re-ranking methods are widely used in many retrieval tasks to i...

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification

Recent works in neural network verification show that cheap incomplete v...

Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures

Point cloud alignment is a common problem in computer vision and robotic...

Please sign up or login with your details

Forgot password? Click here to reset