50 citations · 113 across the 7 of their papers we have counts for
7 papers
Fast Adversarial Training with Adaptive Step Size
Zhichao Huang, Yanbo Fan, Chen Liu +5
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it har…
Black-box Prompt Learning for Pre-trained Language Models
Shizhe Diao, Zhichao Huang, Ruijia Xu +4
The increasing scale of general-purpose Pre-trained Language Models (PLMs) necessitates the study of more efficient adaptation across different downstream tasks. In this paper, we…
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
Chen Liu, Zhichao Huang, Mathieu Salzmann +2
Adversarial training is a popular method to robustify models against adversarial attacks. However, it exhibits much more severe overfitting than training on clean inputs. In this w…
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling
Zhichao Huang, Xintong Han, Jia Xu +1
We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. Despite recent advances in…
CorrAttack: Black-box Adversarial Attack with Structured Search
Zhichao Huang, Yaowei Huang, Tong Zhang
We present a new method for score-based adversarial attack, where the attacker queries the loss-oracle of the target model. Our method employs a parameterized search space with a s…
Black-Box Adversarial Attack with Transferable Model-based Embedding
Zhichao Huang, Tong Zhang
We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a…
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack
Haishan Ye, Zhichao Huang, Cong Fang +2
Zeroth-order optimization is an important research topic in machine learning. In recent years, it has become a key tool in black-box adversarial attack to neural network based imag…