Cover Image

Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Xianlong, Li, Minghui, Liu, Wei, Zhang, Hangtao, Hu, Shengshan, Zhang, Yechao, Zhou, Ziqi, Jin, Hai
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Detecting and Corrupting Convolution-based Unlearnable Examples
by: Li, Minghui, et al.
Published: (2023)

Dual-branch Robust Unlearnable Examples
by: Wang, Xianlong, et al.
Published: (2026)

Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
by: Wang, Yichen, et al.
Published: (2024)

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation
by: Song, Yufei, et al.
Published: (2024)

ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification
by: Wang, Xianlong, et al.
Published: (2024)

Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving
by: Zhang, Hangtao, et al.
Published: (2024)

ADVEDM:Fine-grained Adversarial Attack against VLM-based Embodied Agents
by: Wang, Yichen, et al.
Published: (2025)

Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
by: Zhou, Ziqi, et al.
Published: (2024)

DarkHash: A Data-Free Backdoor Attack Against Deep Hashing
by: Zhou, Ziqi, et al.
Published: (2025)

SegTrans: Transferable Adversarial Examples for Segmentation Models
by: Song, Yufei, et al.
Published: (2025)

Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
by: Zhang, Yechao, et al.
Published: (2023)

Test-Time Backdoor Detection for Object Detection Models
by: Zhang, Hangtao, et al.
Published: (2025)

Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces
by: Shi, Junyu, et al.
Published: (2025)

Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization
by: Zhang, Yechao, et al.
Published: (2025)

Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models
by: Yan, Shihui, et al.
Published: (2026)

Transferable Adversarial Facial Images for Privacy Protection
by: Li, Minghui, et al.
Published: (2024)

Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models
by: A, Pranav, et al.
Published: (2026)

Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving
by: Zhu, Zihui, et al.
Published: (2026)

Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness
by: Song, Yufei, et al.
Published: (2026)

Dual-View Inference Attack: Machine Unlearning Amplifies Privacy Exposure
by: Xue, Lulu, et al.
Published: (2025)

GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation
by: Zhou, Wei, et al.
Published: (2023)

[MASK] is All You Need
by: Hu, Vincent Tao, et al.
Published: (2024)

3D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimation
by: Cheng, Yihua, et al.
Published: (2025)

NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors
by: Zhou, Ziqi, et al.
Published: (2024)

PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
by: Zhang, Tunhou, et al.
Published: (2022)

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
by: Zhou, Da-Wei, et al.
Published: (2023)

Performance is not All You Need: Sustainability Considerations for Algorithms
by: Li, Xiang, et al.
Published: (2025)

Vanish into Thin Air: Cross-prompt Universal Adversarial Attacks for SAM2
by: Zhou, Ziqi, et al.
Published: (2025)

UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion
by: Xue, Lulu, et al.
Published: (2026)

Emu3: Next-Token Prediction is All You Need
by: Wang, Xinlong, et al.
Published: (2024)

Search is All You Need for Few-shot Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)

Multi-View Representation is What You Need for Point-Cloud Pre-Training
by: Yan, Siming, et al.
Published: (2023)

Ideal Registration? Segmentation is All You Need
by: Chen, Xiang, et al.
Published: (2025)

SVAC: Scaling Is All You Need For Referring Video Object Segmentation
by: Zhang, Li, et al.
Published: (2025)

PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via Multi-Modal Distribution Estimation
by: Zhang, Jie, et al.
Published: (2023)

Bytes Are All You Need: Transformers Operating Directly On File Bytes
by: Horton, Maxwell, et al.
Published: (2023)

Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding
by: Qian, Guocheng, et al.
Published: (2022)

Positive Label Is All You Need for Multi-Label Classification
by: Yuan, Zhixiang, et al.
Published: (2023)

Robust Fine-tuning for Pre-trained 3D Point Cloud Models
by: Zhang, Zhibo, et al.
Published: (2024)

LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation
by: Guo, Xianda, et al.
Published: (2024)