  Identifying potential threats concealed within the baggage is of prime
concern for the security staff. Many researchers have developed frameworks that
can detect baggage threats from X-ray scans. However, to the best of our
knowledge, all of these frameworks require extensive training on large-scale
and well-annotated datasets, which are hard to procure in the real world. This
paper presents a novel unsupervised anomaly instance segmentation framework
that recognizes baggage threats, in X-ray scans, as anomalies without requiring
any ground truth labels. Furthermore, thanks to its stylization capacity, the
framework is trained only once, and at the inference stage, it detects and
extracts contraband items regardless of their scanner specifications. Our
one-staged approach initially learns to reconstruct normal baggage content via
an encoder-decoder network utilizing a proposed stylization loss function. The
model subsequently identifies the abnormal regions by analyzing the disparities
within the original and the reconstructed scans. The anomalous regions are then
clustered and post-processed to fit a bounding box for their localization. In
addition, an optional classifier can also be appended with the proposed
framework to recognize the categories of these extracted anomalies. A thorough
evaluation of the proposed system on four public baggage X-ray datasets,
without any re-training, demonstrates that it achieves competitive performance
as compared to the conventional fully supervised methods (i.e., the mean
average precision score of 0.7941 on SIXray, 0.8591 on GDXray, 0.7483 on
OPIXray, and 0.5439 on COMPASS-XP dataset) while outperforming state-of-the-art
semi-supervised and unsupervised baggage threat detection frameworks by 67.37%,
32.32%, 47.19%, and 45.81% in terms of F1 score across SIXray, GDXray, OPIXray,
and COMPASS-XP datasets, respectively.
