Jonas Rauber

is this you? claim profile

0 followers

On Evaluating Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to...
02/18/2019 ∙ by Nicholas Carlini, et al. ∙ 10 ∙ share

read it
Adversarial Vision Challenge

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
08/06/2018 ∙ by Wieland Brendel, et al. ∙ 4 ∙ share

read it
Generalisation in humans and deep neural networks

We compare the robustness of humans and current convolutional deep neura...
08/27/2018 ∙ by Robert Geirhos, et al. ∙ 4 ∙ share

read it
Accurate, reliable and fast robustness evaluation

Throughout the past five years, the susceptibility of neural networks to...
07/01/2019 ∙ by Wieland Brendel, et al. ∙ 3 ∙ share

read it
Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

Even todays most advanced machine learning models are easily fooled by a...
07/13/2017 ∙ by Jonas Rauber, et al. ∙ 0 ∙ share

read it
Comparing deep neural networks against humans: object recognition when the signal gets weaker

Human visual object recognition is typically rapid and seemingly effortl...
06/21/2017 ∙ by Robert Geirhos, et al. ∙ 0 ∙ share

read it
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Many machine learning algorithms are vulnerable to almost imperceptible ...
12/12/2017 ∙ by Wieland Brendel, et al. ∙ 0 ∙ share

read it
Robust Perception through Analysis by Synthesis

The intriguing susceptibility of deep neural networks to minimal input p...
05/23/2018 ∙ by Lukas Schott, et al. ∙ 0 ∙ share

read it
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks

Modern neural networks are highly non-robust against adversarial manipul...
03/27/2019 ∙ by Francesco Croce, et al. ∙ 0 ∙ share

read it
Modeling patterns of smartphone usage and their relationship to cognitive health

The ubiquity of smartphone usage in many people's lives make it a rich s...
11/13/2019 ∙ by Jonas Rauber, et al. ∙ 0 ∙ share

read it

Jonas Rauber

Featured Co-authors

Matthias Hein

Ian Goodfellow

Matthias Bethge

Nicolas Papernot

Nicholas Carlini

Emily B. Fox

Aleksander Madry

Wieland Brendel

Dimitris Tsipras

Marcel Salathe

Francesco Croce

On Evaluating Adversarial Robustness

Adversarial Vision Challenge

Generalisation in humans and deep neural networks

Accurate, reliable and fast robustness evaluation

Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

Comparing deep neural networks against humans: object recognition when the signal gets weaker

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Robust Perception through Analysis by Synthesis

Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks

Modeling patterns of smartphone usage and their relationship to cognitive health