  Due to the steadily rising amount of valuable goods in supply chains,
tampering detection for parcels is becoming increasingly important. In this
work, we focus on the use-case last-mile delivery, where only a single RGB
image is taken and compared against a reference from an existing database to
detect potential appearance changes that indicate tampering. We propose a
tampering detection pipeline that utilizes keypoint detection to identify the
eight corner points of a parcel. This permits applying a perspective
transformation to create normalized fronto-parallel views for each visible
parcel side surface. These viewpoint-invariant parcel side surface
representations facilitate the identification of signs of tampering on parcels
within the supply chain, since they reduce the problem to parcel side surface
matching with pair-wise appearance change detection. Experiments with multiple
classical and deep learning-based change detection approaches are performed on
our newly collected TAMpering detection dataset for PARcels, called TAMPAR. We
evaluate keypoint and change detection separately, as well as in a unified
system for tampering detection. Our evaluation shows promising results for
keypoint (Keypoint AP 75.76) and tampering detection (81% accuracy, F1-Score
0.83) on real images. Furthermore, a sensitivity analysis for tampering types,
lens distortion and viewing angles is presented. Code and dataset are available
at https://a-nau.github.io/tampar.
