  Transferability of adversarial examples is of central importance for
attacking an unknown model, which facilitates adversarial attacks in more
practical scenarios, e.g., black-box attacks. Existing transferable attacks
tend to craft adversarial examples by indiscriminately distorting features to
degrade prediction accuracy in a source model without aware of intrinsic
features of objects in the images. We argue that such brute-force degradation
would introduce model-specific local optimum into adversarial examples, thus
limiting the transferability. By contrast, we propose the Feature
Importance-aware Attack (FIA), which disrupts important object-aware features
that dominate model decisions consistently. More specifically, we obtain
feature importance by introducing the aggregate gradient, which averages the
gradients with respect to feature maps of the source model, computed on a batch
of random transforms of the original clean image. The gradients will be highly
correlated to objects of interest, and such correlation presents invariance
across different models. Besides, the random transforms will preserve intrinsic
features of objects and suppress model-specific information. Finally, the
feature importance guides to search for adversarial examples towards disrupting
critical features, achieving stronger transferability. Extensive experimental
evaluation demonstrates the effectiveness and superior performance of the
proposed FIA, i.e., improving the success rate by 9.5% against normally trained
models and 12.8% against defense models as compared to the state-of-the-art
transferable attacks. Code is available at: https://github.com/hcguoO0/FIA
