8 citations · 22 across the 7 of their papers we have counts for
9 papers · 1 filter
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…
Human Body Measurement Estimation with Adversarial Augmentation
Nataniel Ruiz, Miriam Bellver, Timo Bolkart +4
We present a Body Measurement network (BMnet) for estimating 3D anthropomorphic measurements of the human body shape from silhouette images. Training of BMnet is performed on data…
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,…
MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias
Nataniel Ruiz, Barry-John Theobald, Anurag Ranjan +2
To detect bias in face recognition networks, it can be useful to probe a network under test using samples in which only specific attributes vary in some controlled way. However, ca…
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…