  We propose to utilize gradients for detecting adversarial and
out-of-distribution samples. We introduce confounding labels -- labels that
differ from normal labels seen during training -- in gradient generation to
probe the effective expressivity of neural networks. Gradients depict the
amount of change required for a model to properly represent given inputs,
providing insight into the representational power of the model established by
network architectural properties as well as training data. By introducing a
label of different design, we remove the dependency on ground truth labels for
gradient generation during inference. We show that our gradient-based approach
allows for capturing the anomaly in inputs based on the effective expressivity
of the models with no hyperparameter tuning or additional processing, and
outperforms state-of-the-art methods for adversarial and out-of-distribution
detection.
