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Exposing Previously Undetectable Faults in Deep Neural Networks
Existing methods for testing DNNs solve the oracle problem by constraining the raw features (e. g. image pixel values) to be within a small distance of a dataset example for which the desired DNN output is known.
Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities
We cannot guarantee that training datasets are representative of the distribution of inputs that will be encountered during deployment.
Adaptive Generation of Unrestricted Adversarial Inputs
Neural networks are vulnerable to adversarially-constructed perturbations of their inputs.
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