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Found 6 papers, 1 papers with code
Attention for Adversarial Attacks: Learning from your Mistakes
The GNN outputs a smaller subspace for the PGD attack to focus on.
Improving Local Effectiveness for Global robust training
However, many of them rely on strong adversaries, which can be prohibitively expensive to generate when the input dimension is high and the model structure is complicated.
Neural Network Branch-and-Bound for Neural Network Verification
Our combined framework achieves a 50\% reduction in both the number of branches and the time required for verification on various convolutional networks when compared to several state-of-the-art verification methods.
Improving Local Effectiveness for Global Robustness Training
We demonstrate that, by maximizing the use of adversaries, we achieve high robust accuracy with weak adversaries.
Neural Network Branching for Neural Network Verification
Empirically, our framework achieves roughly $50\%$ reduction in both the number of branches and the time required for verification on various convolutional networks when compared to the best available hand-designed branching strategy.
Branch and Bound for Piecewise Linear Neural Network Verification
We use the data sets to conduct a thorough experimental comparison of existing and new algorithms and to provide an inclusive analysis of the factors impacting the hardness of verification problems.
