50 citations · 52 across the 3 of their papers we have counts for
5 papers
Localized Uncertainty Attacks
Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas +3
The susceptibility of deep learning models to adversarial perturbations has stirred renewed attention in adversarial examples resulting in a number of attacks. However, most of the…
Enabling Inference Privacy with Adaptive Noise Injection
Sanjay Kariyappa, Ousmane Dia, Moinuddin K Qureshi
User-facing software services are becoming increasingly reliant on remote servers to host Deep Neural Network (DNN) models, which perform inference tasks for the clients. Such serv…
Adversarial Examples in Modern Machine Learning: A Review
Rey Reza Wiyatno, Anqi Xu, Ousmane Dia +1
Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to p…
Semantics Preserving Adversarial Learning
Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad
While progress has been made in crafting visually imperceptible adversarial examples, constructing semantically meaningful ones remains a challenge. In this paper, we propose a fra…
Bayesian Model-Agnostic Meta-Learning
Taesup Kim, Jaesik Yoon, Ousmane Dia +3
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper,…