25 citations · 53 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
Kevin Eykholt, Atul Prakash
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlate…