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20212025
most citedLong-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

Gradient-Free Adversarial Purification with Diffusion Models

Xuelong Dai, Dong Wang, Xiuzhen Cheng +1

Adversarial training and adversarial purification are two widely used defense strategies for enhancing model robustness against adversarial attacks. However, adversarial training r…

cs.CV2024

Improving Transferable Targeted Attacks with Feature Tuning Mixup

Kaisheng Liang, Xuelong Dai, Yanjie Li +2

Deep neural networks (DNNs) exhibit vulnerability to adversarial examples that can transfer across different DNN models. A particularly challenging problem is developing transferab…

cs.CV2024

Transferable 3D Adversarial Shape Completion using Diffusion Models

Xuelong Dai, Bin Xiao

Recent studies that incorporate geometric features and transformers into 3D point cloud feature learning have significantly improved the performance of 3D deep-learning models. How…

cs.LG2023★ 1 cited

AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models

Xuelong Dai, Kaisheng Liang, Bin Xiao

Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applicati…

cs.CV2022

Physical-World Optical Adversarial Attacks on 3D Face Recognition

Yanjie Li, Yiquan Li, Xuelong Dai +2

2D face recognition has been proven insecure for physical adversarial attacks. However, few studies have investigated the possibility of attacking real-world 3D face recognition sy…

cs.CV2021★ 1 cited

Generating Unrestricted 3D Adversarial Point Clouds

Xuelong Dai, Yanjie Li, Hua Dai +1

Utilizing 3D point cloud data has become an urgent need for the deployment of artificial intelligence in many areas like facial recognition and self-driving. However, deep learning…

cs.LG2021★ 2 cited

Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks

Fan Liu, Shuyu Zhao, Xuelong Dai +1

Meta-learning model can quickly adapt to new tasks using few-shot labeled data. However, despite achieving good generalization on few-shot classification tasks, it is still challen…