27 citations · 29 across the 3 of their papers we have counts for
6 papers
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…
Boosting Adversarial Transferability through Enhanced Momentum
Xiaosen Wang, Jiadong Lin, Han Hu +2
Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack metho…
Enhancing the Transferability of Adversarial Attacks through Variance Tuning
Xiaosen Wang, Kun He
Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success ra…
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…
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks
Xiaosen Wang, Yichen Yang, Yihe Deng +1
Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, exis…
A New Anchor Word Selection Method for the Separable Topic Discovery
Kun He, Wu Wang, Xiaosen Wang +1
Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a…