67 citations · 129 across the 22 of their papers we have counts for
5 papers · 1 filter
SegNBDT: Visual Decision Rules for Segmentation
Alvin Wan, Daniel Ho, Younjin Song +3
The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks li…
Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks
Nataniel Ruiz, Sarah Adel Bargal, Stan Sclaroff
In this work, we develop efficient disruptions of black-box image translation deepfake generation systems. We are the first to demonstrate black-box deepfake generation disruption…
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
Nataniel Ruiz, Sarah Adel Bargal, Stan Sclaroff
Face modification systems using deep learning have become increasingly powerful and accessible. Given images of a person's face, such systems can generate new images of that same p…
NBDT: Neural-Backed Decision Trees
Alvin Wan, Lisa Dunlap, Daniel Ho +6
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work comb…
Explainable Deep Classification Models for Domain Generalization
Andrea Zunino, Sarah Adel Bargal, Riccardo Volpi +5
Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object…