TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply
  Chains
Alexander Naumann Felix Hertlein Laura Dörr Kai Furmans
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.