37 citations · 106 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Beware of the App! On the Vulnerability Surface of Smart Devices through their Companion Apps
Davino Mauro Junior, Luis Melo, Harvey Lu +2
Internet of Things (IoT) devices are becoming increasingly important. These devices are often resource-limited, hindering rigorous enforcement of security policies. Assessing the v…