10 citations · 25 across the 7 of their papers we have counts for
13 papers
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Nataniel Ruiz, Sarah Adel Bargal, Cihang Xie +2
Modern deep neural networks tend to be evaluated on static test sets. One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness…
Temporal Relevance Analysis for Video Action Models
Quanfu Fan, Donghyun Kim, Chun-Fu +4
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to…
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
Benjamin Spetter-Goldstein, Nataniel Ruiz, Sarah Adel Bargal
The modern open internet contains billions of public images of human faces across the web, especially on social media websites used by half the world's population. In this context,…
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…