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Adversarial examples are malicious inputs crafted to inducemisclassification. Commonly studied sensitivity-based adversarial examplesintroduce semantically-small changes to an input that result in a differentmodel prediction. This paper studies a complementary failure mode,invariance-based adversarial examples, that introduce minimal semantic changesthat modify an input's true label yet preserve the model's prediction. Wedemonstrate fundamental tradeoffs between these two types of adversarialexamples.  We show that defenses against sensitivity-based attacks actively harm amodel's accuracy on invariance-based attacks, and that new approaches areneeded to resist both attack types. In particular, we break state-of-the-artadversarially-trained and certifiably-robust models by generating smallperturbations that the models are (provably) robust to, yet that change aninput's class according to human labelers. Finally, we formally show that theexistence of excessively invariant classifiers arises from the presence ofoverly-robust predictive features in standard datasets.

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations