# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 6 Conclusions  
This paper introduces a novel and advanced method for performing black-box transfer query attacks on point cloud data. The method employs a powerful autoencoder for point cloud reconstruction, combined with a global attack strategy based on the Adam optimizer, designed to disrupt the neighborhood information of surface points within the point cloud. Specifically, we implement global attack strategies by constructing a new autoencoder with a multi-head architecture, which disturbs the local geometric structure of surface points, thereby enhancing the effectiveness of the attack. Additionally, we introduce secondary \"modifications\" to key points using a novel point cloud sensitivity map. Simultaneously, local attacks are carried out using the SimBA method, utilizing Integrated Gradients (IGs) and tangential synthesis directions. This approach strikes a balance between the invisibility and effectiveness of the attack, ensuring that the adversarial samples remain sufficiently concealed while still significantly impairing the performance of the target model. Our method demonstrates excellent transferability, maintaining a high Attack Success Rate (ASR) across different models and environments. After testing against specially designed defense mechanisms for adversarial 3D samples, our attack method successfully bypasses these defenses, significantly improving the ASR. These experimental results validate the effectiveness and robustness of our approach. Looking ahead, we plan to release the generated adversarial point clouds on the Amazon Mechanical Turk platform to collect human user preference data, which will support future qualitative evaluations. This will help us gain deeper insights into human perception differences and reactions to adversarial samples in 3D point clouds. Furthermore, we will organize the collected adversarial samples into the ModelNet40 attack dataset and release it publicly, providing a valuable resource for the academic community and further promoting research on classifier robustness and interpretability. Building on this, our goal \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Target Model** & **Attack** & **ASR** & **CD** & **HD** & **L2** & **AT** \\ \hline \multirow{4}{*}{PointNet} & Gauss Noise (\(\mu=0\), \(\sigma=0.01\)) & 38.65\% & 0.00142 & 0.07518 & 0.43488 & 21.68067 \\  & Gauss Noise (\(\mu=0\), \(\sigma=0.1\)) & 38.65\% & 0.00142 & 0.07518 & 0.43488 & 15.85776 \\  & Random Noise (\(\eta=0.5\)) & 38.81\% & 0.00142 & 0.07518 & 0.43491 & 13.22587 \\  & Random Noise (\(\eta=0.8\)) & 38.70\% & 0.00142 & 0.0752 & 0.4349 & 12.17309 \\ \hline \multirow{4}{*}{DGCNN} & Gauss Noise (\(\mu=0\), \(\sigma=0.01\)) & 58.87\% & 0.00197 & 0.07824 & 0.43535 & 10.69092 \\  & Gauss Noise (\(\mu=0\), \(\sigma=0.1\)) & 58.87\% & 0.00197 & 0.07824 & 0.43535 & 8.95007 \\  & Random Noise (\(\eta=0.5\)) & 58.67\% & 0.00198 & 0.07823 & 0.43536 & 11.80419 \\  & Random Noise (\(\eta=0.8\)) & 58.87\% & 0.00198 & 0.07824 & 0.43536 & 9.97715 \\ \hline \multirow{4}{*}{CurveNet} & Gauss Noise (\(\mu=0\), \(\sigma=0.01\)) & 64.58\% & 0.00191 & 0.07835 & 0.43522 & 24.13438 \\  & Gauss Noise (\(\mu=0\), \(\sigma=0.1\)) & 64.60\% & 0.00191 & 0.07845 & 0.4356 & 23.58111 \\ \cline{1-1}  & Random Noise (\(\eta=0.5\)) & 64.71\% & 0.00191 & 0.07837 & 0.43523 & 22.96008 \\ \cline{1-1}  & Random Noise (\(\eta=0.8\)) & 63.09\% & 0.00192 & 0.07865 & 0.43735 & 20.45849 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation studies with different noise. is to continue proposing and deploying high-quality 3D point cloud attack methods. This will not only enhance the diversity and accuracy of attack effectiveness but also drive deeper advancements in adversarial sample research and defense strategies in this field. Through these efforts, we aim to advance 3D point cloud attack techniques and provide stronger theoretical support and practical references for research in related areas. Methodology, G.C. ; validation, G.C. and T.L. ; writing--original draft preparation, G.C. and C.L.