  Deep neural networks (DNN) can achieve high performance when applied to
In-Distribution (ID) data which come from the same distribution as the training
set. When presented with anomaly inputs not from the ID, the outputs of a DNN
should be regarded as meaningless. However, modern DNN often predict anomaly
inputs as an ID class with high confidence, which is dangerous and misleading.
In this work, we consider three classes of anomaly inputs, (1) natural inputs
from a different distribution than the DNN is trained for, known as
Out-of-Distribution (OOD) samples, (2) crafted inputs generated from ID by
attackers, often known as adversarial (AD) samples, and (3) noise (NS) samples
generated from meaningless data. We propose a framework that aims to detect all
these anomalies for a pre-trained DNN. Unlike some of the existing works, our
method does not require preprocessing of input data, nor is it dependent to any
known OOD set or adversarial attack algorithm. Through extensive experiments
over a variety of DNN models for the detection of aforementioned anomalies, we
show that in most cases our method outperforms state-of-the-art anomaly
detection methods in identifying all three classes of anomalies.
