  Detecting anomalous inputs, such as adversarial and out-of-distribution (OOD)
inputs, is critical for classifiers (including deep neural networks or DNNs)
deployed in real-world applications. While prior works have proposed various
methods to detect such anomalous samples using information from the internal
layer representations of a DNN, there is a lack of consensus on a principled
approach for the different components of such a detection method. As a result,
often heuristic and one-off methods are applied for different aspects of this
problem. We propose an unsupervised anomaly detection framework based on the
internal DNN layer representations in the form of a meta-algorithm with
configurable components. We proceed to propose specific instantiations for each
component of the meta-algorithm based on ideas grounded in statistical testing
and anomaly detection. We evaluate the proposed methods on well-known image
classification datasets with strong adversarial attacks and OOD inputs,
including an adaptive attack that uses the internal layer representations of
the DNN (often not considered in prior work). Comparisons with five
recently-proposed competing detection methods demonstrates the effectiveness of
our method in detecting adversarial and OOD inputs.
