25 citations · 53 across the 5 of their papers we have counts for
11 papers
Understanding and Diagnosing Vulnerability under Adversarial Attacks
Haizhong Zheng, Ziqi Zhang, Honglak Lee +1
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Currently, there is no clear insight into how slight perturbations cause such a large difference in c…
Towards Robustness against Unsuspicious Adversarial Examples
Liang Tong, Minzhe Guo, Atul Prakash +1
Despite the remarkable success of deep neural networks, significant concerns have emerged about their robustness to adversarial perturbations to inputs. While most attacks aim to e…
Can Attention Masks Improve Adversarial Robustness?
Pratik Vaishnavi, Tianji Cong, Kevin Eykholt +2
Deep Neural Networks (DNNs) are known to be susceptible to adversarial examples. Adversarial examples are maliciously crafted inputs that are designed to fool a model, but appear n…
Efficient Adversarial Training with Transferable Adversarial Examples
Haizhong Zheng, Ziqi Zhang, Juncheng Gu +2
Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require or…
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders
Pratik Vaishnavi, Kevin Eykholt, Atul Prakash +1
Adversarial machine learning is a well-studied field of research where an adversary causes predictable errors in a machine learning algorithm through precise manipulation of the in…
Analyzing the Interpretability Robustness of Self-Explaining Models
Haizhong Zheng, Earlence Fernandes, Atul Prakash
Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness. We evaluate the interpretability robu…