Search Results for author: Hanieh Naderi

Found 6 papers, 1 papers with code

Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey

no code implementations1 Jul 2023 Hanieh Naderi, Ivan V. Bajić

To encourage future research, this survey summarizes the current progress on adversarial attack and defense techniques on point cloud classification.

3D Point Cloud Classification Adversarial Attack +1

Model-Free Prediction of Adversarial Drop Points in 3D Point Clouds

no code implementations19 Oct 2022 Hanieh Naderi, Chinthaka Dinesh, Ivan V. Bajic, Shohreh Kasaei

To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for model-free adversarial point prediction, and which combination of features is best suited for this purpose.

Decision Making

Triple Motion Estimation and Frame Interpolation based on Adaptive Threshold for Frame Rate Up-Conversion

no code implementations5 Mar 2022 Hanieh Naderi, Mohammad Rahmati

The proposed algorithm creates interpolated frames by first estimating motion vectors using unilateral (jointing forward and backward) and bilateral motion estimation.

Motion Estimation

LPF-Defense: 3D Adversarial Defense based on Frequency Analysis

2 code implementations23 Feb 2022 Hanieh Naderi, Kimia Noorbakhsh, Arian Etemadi, Shohreh Kasaei

Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks.

3D Point Cloud Classification Adversarial Defense +1

Adversarial Attack by Limited Point Cloud Surface Modifications

no code implementations7 Oct 2021 Atrin Arya, Hanieh Naderi, Shohreh Kasaei

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.

Adversarial Attack Point Cloud Classification +1

Generating Unrestricted Adversarial Examples via Three Parameters

no code implementations13 Mar 2021 Hanieh Naderi, Leili Goli, Shohreh Kasaei

It also reduces the model accuracy by an average of 73% on six datasets MNIST, FMNIST, SVHN, CIFAR10, CIFAR100, and ImageNet.

Adversarial Attack

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