230 citations · 342 across the 7 of their papers we have counts for
7 papers
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…
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.…
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…
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…
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…
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…
Improving the Generalization of Adversarial Training with Domain Adaptation
Chuanbiao Song, Kun He, Liwei Wang +1
By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial tra…