2 citations · 3 across the 4 of their papers we have counts for
6 papers
Triggering hallucinations in model-based MRI reconstruction via adversarial perturbations
Suna Buğday, Yvan Saeys, Jonathan Peck
Generative models are increasingly used to improve the quality of medical imaging, such as reconstruction of magnetic resonance images and computed tomography. However, it is well-…
Robust width: A lightweight and certifiable adversarial defense
Jonathan Peck, Bart Goossens
Deep neural networks are vulnerable to so-called adversarial examples: inputs which are intentionally constructed to cause the model to make incorrect predictions or classification…
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems
Arne Gevaert, Jonathan Peck, Yvan Saeys
Deep Reinforcement Learning uses a deep neural network to encode a policy, which achieves very good performance in a wide range of applications but is widely regarded as a black bo…
Regional Image Perturbation Reduces Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Utku Ozbulak, Jonathan Peck, Wesley De Neve +3
Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks. In thi…
Inline Detection of DGA Domains Using Side Information
Raaghavi Sivaguru, Jonathan Peck, Femi Olumofin +2
Malware applications typically use a command and control (C&C) server to manage bots to perform malicious activities. Domain Generation Algorithms (DGAs) are popular methods for ge…
CharBot: A Simple and Effective Method for Evading DGA Classifiers
Jonathan Peck, Claire Nie, Raaghavi Sivaguru +5
Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names which can be used for command and control (C&C) purposes. Approaches based on…