activity
20182026
most citedRegional Adversarial Training for Better Robust Generalization

5 citations · 8 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2026

NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild

Aleksandr Gushchin, Khaled Abud, Ekaterina Shumitskaya +51

This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal o…

cs.CV20211 cited

Adversarial Attacks on ML Defense Models Competition

Yinpeng Dong, Qi-An Fu, Xiao Yang +25

Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…

cs.CV20215 cited

Regional Adversarial Training for Better Robust Generalization

Chuanbiao Song, Yanbo Fan, Yichen Yang +4

Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks. To our knowledge, existing AT-based methods usuall…

cs.LG20212 cited

Multi-stage Optimization based Adversarial Training

Xiaosen Wang, Chuanbiao Song, Liwei Wang +1

In the field of adversarial robustness, there is a common practice that adopts the single-step adversarial training for quickly developing adversarially robust models. However, the…

cs.LG2019

Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Jiadong Lin, Chuanbiao Song, Kun He +2

Deep learning models are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on benign inputs. However, under the black-box setting, most exist…

cs.CV2019

AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples

Xiaosen Wang, Kun He, Chuanbiao Song +2

Despite the rapid development of adversarial machine learning, most adversarial attack and defense researches mainly focus on the perturbation-based adversarial examples, which is…