401 citations · 2.4k across the 26 of their papers we have counts for
3 papers · 1 filter
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Sven Gowal, Jonathan Uesato, Chongli Qin +3
Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be miscla…
Are Labels Required for Improving Adversarial Robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang +3
Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets…
Verification of Non-Linear Specifications for Neural Networks
Chongli Qin, Krishnamurthy, Dvijotham +7
Prior work on neural network verification has focused on specifications that are linear functions of the output of the network, e.g., invariance of the classifier output under adve…