1 citations · 1 across the 3 of their papers we have counts for
3 papers
Distilling Adversarial Robustness Using Heterogeneous Teachers
Jieren Deng, Aaron Palmer, Rigel Mahmood +4
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e.g., self-…
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks
Kaleel Mahmood, Rigel Mahmood, Ethan Rathbun +1
The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018. This massive information output has yet to be…
On the Robustness of Vision Transformers to Adversarial Examples
Kaleel Mahmood, Rigel Mahmood, Marten van Dijk
Recent advances in attention-based networks have shown that Vision Transformers can achieve state-of-the-art or near state-of-the-art results on many image classification tasks. Th…