5 citations · 17 across the 6 of their papers we have counts for
6 papers
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The transferability of adversarial examples across deep neural networks (DNNs) is the crux of many black-box attacks. Many prior efforts have been devoted to improving the transfer…
Squeeze Training for Adversarial Robustness
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and…
Yet Another Intermediate-Level Attack
Qizhang Li, Yiwen Guo, Hao Chen
The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. In this paper, we propose a novel method to enha…
A Normality Test for High-dimensional Data based on a Nearest Neighbor Approach
Hao Chen, Yin Xia
Many statistical methodologies for high-dimensional data assume the population is normal. Although a few multivariate normality tests have been proposed, to the best of our knowled…
Change-point detection for multivariate and non-Euclidean data with local dependency
Hao Chen
In a sequence of multivariate observations or non-Euclidean data objects, such as networks, local dependence is common and could lead to false change-point discoveries. We propose…
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks
Yongshuai Liu, Jiyu Chen, Hao Chen
Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either changing the network architecture or augmenting th…