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

Abstract

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.

Model Overview

We present a tampering detection pipeline, which combines keypoint detection with change detection approaches.

Figure: We detect tampering by comparing the full parcel texture from a database (a) with the viewpoint-invariant parcel side surfaces of a single image by exploiting parcel corner point predictions (b). Appearance change detection is performed for each pair of matching parcel side surfaces to identify tampering (c). © IEEE 2024.

TAMPAR Dataset

We introduce TAMPAR, a novel real-world dataset of parcels with:

>900 annotated real-world images with >2,700 visible parcel side surfaces
6 different tampering types
6 different distortion strengths

Randomly sampled images from TAMPAR.

Normalized parcel views of the above image samples.

BibTeX


@inproceedings{naumannTAMPAR2024,
    author    = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai},
    title     = {TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
    month     = {January},
    year      = {2024}
}