4 papers
Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models
Sohaib Ahmad, Benjamin Fuller, Kaleel Mahmood
Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a pr…
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…
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…