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Found 6 papers, 1 papers with code
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey
To encourage future research, this survey summarizes the current progress on adversarial attack and defense techniques on point cloud classification. This paper first introduces the principles and characteristics of adversarial attacks and summarizes and analyzes adversarial example generation methods in recent years.
No-Box Attacks on 3D Point Cloud Classification
To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for adversarial point prediction, and which combination of features is best suited for this purpose.
Triple Motion Estimation and Frame Interpolation based on Adaptive Threshold for Frame Rate Up-Conversion
The proposed algorithm creates interpolated frames by first estimating motion vectors using unilateral (jointing forward and backward) and bilateral motion estimation.
LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks.
Adversarial Attack by Limited Point Cloud Surface Modifications
The obtained results show that it can perform successful attacks and achieve state-of-the-art results by only a limited number of point modifications while preserving the appearance of the point cloud.
Generating Unrestricted Adversarial Examples via Three Parameters
It also reduces the model accuracy by an average of 73% on six datasets MNIST, FMNIST, SVHN, CIFAR10, CIFAR100, and ImageNet.
