This paper presents a novel universal perturbation method for generating
robust multi-view adversarial examples in 3D object recognition. Unlike
conventional attacks limited to single views, our approach operates on multiple
2D images, offering a practical and scalable solution for enhancing model
scalability and robustness. This generalizable method bridges the gap between
2D perturbations and 3D-like attack capabilities, making it suitable for
real-world applications.
Existing adversarial attacks may become ineffective when images undergo
transformations like changes in lighting, camera position, or natural
deformations. We address this challenge by crafting a single universal noise
perturbation applicable to various object views. Experiments on diverse
rendered 3D objects demonstrate the effectiveness of our approach. The
universal perturbation successfully identified a single adversarial noise for
each given set of 3D object renders from multiple poses and viewpoints.
Compared to single-view attacks, our universal attacks lower classification
confidence across multiple viewing angles, especially at low noise levels. A
sample implementation is made available at
this https URL

本研究提出了一种新颖的通用干扰方法，用于生成 3D 物体识别中的强健的多视角对抗样本。与仅限于单个视角的传统攻击不同，我们的方法在多个 2D 图像上操作，为增强模型的可扩展性和鲁棒性提供实用且可扩展的解决方案。这种通用方法将 2D 干扰和类似 3D 攻击能力之间的差距缩小，适用于实际应用。

一种噪音统治一切：具有通用扰动的多视角对抗攻击

One Noise to Rule Them All: Multi-View Adversarial Attacks with  Universal Perturbation

Shape completion, i.e., predicting the complete geometry of an object from a
partial observation, is highly relevant for several downstream tasks, most
notably robotic manipulation. When basing planning or prediction of real grasps
on object shape reconstruction, an indication of severe geometric uncertainty
is indispensable. In particular, there can be an irreducible uncertainty in
extended regions about the presence of entire object parts when given ambiguous
object views. To treat this important case, we propose two novel methods for
predicting such uncertain regions as straightforward extensions of any method
for predicting local spatial occupancy, one through postprocessing occupancy
scores, the other through direct prediction of an uncertainty indicator. We
compare these methods together with two known approaches to probabilistic shape
completion. Moreover, we generate a dataset, derived from ShapeNet, of
realistically rendered depth images of object views with ground-truth
annotations for the uncertain regions. We train on this dataset and test each
method in shape completion and prediction of uncertain regions for known and
novel object instances and on synthetic and real data. While direct uncertainty
prediction is by far the most accurate in the segmentation of uncertain
regions, both novel methods outperform the two baselines in shape completion
and uncertain region prediction, and avoiding the predicted uncertain regions
increases the quality of grasps for all tested methods. Web:
this https URL

通过两种方法预测局部空间占用与直接确定性预测，提高不确定区域的分割准确性、完成物体形状，增加抓取质量。