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Due to the steadily rising amount of valuable goods in supply chains,tampering detection for parcels is becoming increasingly important. In thiswork, we focus on the use-case last-mile delivery, where only a single RGBimage is taken and compared against a reference from an existing database todetect potential appearance changes that indicate tampering. We propose atampering detection pipeline that utilizes keypoint detection to identify theeight corner points of a parcel. This permits applying a perspectivetransformation to create normalized fronto-parallel views for each visibleparcel side surface. These viewpoint-invariant parcel side surfacerepresentations facilitate the identification of signs of tampering on parcelswithin the supply chain, since they reduce the problem to parcel side surfacematching with pair-wise appearance change detection. Experiments with multipleclassical and deep learning-based change detection approaches are performed onour newly collected TAMpering detection dataset for PARcels, called TAMPAR. Weevaluate keypoint and change detection separately, as well as in a unifiedsystem for tampering detection. Our evaluation shows promising results forkeypoint (Keypoint AP 75.76) and tampering detection (81% accuracy, F1-Score0.83) on real images. Furthermore, a sensitivity analysis for tampering types,lens distortion and viewing angles is presented. Code and dataset are availableat https://a-nau.github.io/tampar.

TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains