  In explainable artificial intelligence, discriminative feature localization
is critical to reveal a blackbox model's decision-making process from raw data
to prediction. In this article, we use two real datasets, the MNIST handwritten
digits and MIT-BIH Electrocardiogram (ECG) signals, to motivate key
characteristics of discriminative features, namely adaptiveness, predictive
importance and effectiveness. Then, we develop a localization framework based
on adversarial attacks to effectively localize discriminative features. In
contrast to existing heuristic methods, we also provide a statistically
guaranteed interpretability of the localized features by measuring a
generalized partial $R^2$. We apply the proposed method to the MNIST dataset
and the MIT-BIH dataset with a convolutional auto-encoder. In the first, the
compact image regions localized by the proposed method are visually appealing.
Similarly, in the second, the identified ECG features are biologically
plausible and consistent with cardiac electrophysiological principles while
locating subtle anomalies in a QRS complex that may not be discernible by the
naked eye. Overall, the proposed method compares favorably with
state-of-the-art competitors. Accompanying this paper is a Python library
dnn-locate (https://dnn-locate.readthedocs.io/en/latest/) that implements the
proposed approach.
