  Generative models based on variational autoencoders are a popular technique
for detecting anomalies in images in a semi-supervised context. A common
approach employs the anomaly score to detect the presence of anomalies, and it
is known to reach high level of accuracy on benchmark datasets. However, since
anomaly scores are computed from reconstruction disparities, they often obscure
the detection of various spurious features, raising concerns regarding their
actual efficacy. This case study explores the robustness of an anomaly
detection system based on variational autoencoder generative models through the
use of eXplainable AI methods. The goal is to get a different perspective on
the real performances of anomaly detectors that use reconstruction differences.
In our case study we discovered that, in many cases, samples are detected as
anomalous for the wrong or misleading factors.
