4 citations · 13 across the 19 of their papers we have counts for
7 papers · 1 filter
Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems
Khaleque Md Aashiq Kamal, Surya Eada, Aayushi Verma +4
Developments in the machine learning voting domain have shown both promising results and risks. Trained models perform well on ballot classification tasks (> 99% accuracy) but are…
Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack
Nicole Meng, Caleb Manicke, David Chen +4
Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-b…
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness
Jieren Deng, Hanbin Hong, Aaron Palmer +5
Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations. Current approaches for achievi…
Attacking All Tasks at Once Using Adversarial Examples in Multi-Task Learning
Lijun Zhang, Xiao Liu, Kaleel Mahmood +2
Visual content understanding frequently relies on multi-task models to extract robust representations of a single visual input for multiple downstream tasks. However, in comparison…
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Ethan Rathbun, Kaleel Mahmood, Sohaib Ahmad +2
Recent advances in adversarial machine learning have shown that defenses considered to be robust are actually susceptible to adversarial attacks which are specifically customized t…
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples
Kaleel Mahmood, Deniz Gurevin, Marten van Dijk +1
Many defenses have recently been proposed at venues like NIPS, ICML, ICLR and CVPR. These defenses are mainly focused on mitigating white-box attacks. They do not properly examine…
BUZz: BUffer Zones for defending adversarial examples in image classification
Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen +2
We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination…