401 citations · 2.4k across the 26 of their papers we have counts for
5 papers · 1 filter
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato +1
State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial traini…
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue +3
While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where…
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth +6
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minim…
Training verified learners with learned verifiers
Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth +4
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-…
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord +1
This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate 'adversarial risk' as an objectiv…