1 citations · 2 across the 10 of their papers we have counts for
5 papers · 1 filter
Scanning Trojaned Models Using Out-of-Distribution Samples
Hossein Mirzaei, Ali Ansari, Bahar Dibaei Nia +10
Scanning for trojan (backdoor) in deep neural networks is crucial due to their significant real-world applications. There has been an increasing focus on developing effective gener…
Killing it with Zero-Shot: Adversarially Robust Novelty Detection
Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi +3
Novelty Detection (ND) plays a crucial role in machine learning by identifying new or unseen data during model inference. This capability is especially important for the safe and r…
Backdooring Outlier Detection Methods: A Novel Attack Approach
ZeinabSadat Taghavi, Hossein Mirzaei
There have been several efforts in backdoor attacks, but these have primarily focused on the closed-set performance of classifiers (i.e., classification). This has left a gap in ad…
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
Hossein Mirzaei, Mackenzie W. Mathis
Despite significant advancements in out-of-distribution (OOD) detection, existing methods still struggle to maintain robustness against adversarial attacks, compromising their reli…
Universal Novelty Detection Through Adaptive Contrastive Learning
Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari +5
Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality, which can be interpre…