5 citations · 11 across the 7 of their papers we have counts for
14 papers
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Yifeng Xiong +2
Deep neural networks have shown to be very vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to benign inputs. After achieving impressive attac…
An Efficient Algorithm for the Partitioning Min-Max Weighted Matching Problem
Yuxuan Wang, Jinyao Xie, Jiongzhi Zheng +1
The Partitioning Min-Max Weighted Matching (PMMWM) problem is an NP-hard problem that combines the problem of partitioning a group of vertices of a bipartite graph into disjoint su…
A Strengthened Branch and Bound Algorithm for the Maximum Common (Connected) Subgraph Problem
Jianrong Zhou, Kun He, Jiongzhi Zheng +2
We propose a new and strengthened Branch-and-Bound (BnB) algorithm for the maximum common (connected) induced subgraph problem based on two new operators, Long-Short Memory (LSM) a…
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…
Admix: Enhancing the Transferability of Adversarial Attacks
Xiaosen Wang, Xuanran He, Jingdong Wang +1
Deep neural networks are known to be extremely vulnerable to adversarial examples under white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) mod…