13 citations · 14 across the 4 of their papers we have counts for
4 papers
SoK: Colluding Adversaries in Machine Learning Pipelines
Vasisht Duddu, Lipeng He, Asim Waheed +1
Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objectives, knowledge, and capabili…
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Asim Waheed, Vasisht Duddu, Sebastian Szyller
Machine learning (ML) models are susceptible to various risks to security, privacy, and fairness. Most defenses are designed to protect against each risk individually (intended int…
GrOVe: Ownership Verification of Graph Neural Networks using Embeddings
Asim Waheed, Vasisht Duddu, N. Asokan
Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as…
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
Ahmadreza Azizi, Ibrahim Asadullah Tahmid, Asim Waheed +5
Deep Neural Network (DNN) classifiers are known to be vulnerable to Trojan or backdoor attacks, where the classifier is manipulated such that it misclassifies any input containing…