The Limitations of Model Uncertainty in Adversarial SettingsThe Limitations of Model Uncertainty in Adversarial SettingsGrosse, Kathrin and Pfaff, David and Smith, Michael T. and Backes, Michael2018
Paper summarydavidstutzGrosse et al. show that Gaussian Processes allow to reject some adversarial examples based on their confidence and uncertainty; however, attacks maximizing confidence and minimizing uncertainty are still successful. While some state-of-the-art adversarial examples seem to result in significantly different confidence and uncertainty estimates compared to benign examples, Gaussian Processes can still be fooled through particularly crafted adversarial examples. To this end, the confidence is explicitly maximized and, additionally, the uncertainty is constrained to not be larger than the uncertainty of the corresponding benign test example. In experiments, this attack is shown to successfully fool Gaussian Processes while resulting in imperceptible perturbations.
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