{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tampar-visual-tampering-detection-for-parcel","title":"TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains","arxiv_id":"2311.03124","date":"2023-11-06","proceeding":null,"authors":["Alexander Naumann","Felix Hertlein","Laura Dörr","Kai Furmans"],"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.","url_abs":"https://arxiv.org/abs/2311.03124v1","url_pdf":"https://arxiv.org/pdf/2311.03124v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tampar-visual-tampering-detection-for-parcel","repo_url":"https://github.com/a-nau/tampar","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"keypoint-detection","task_name":"Keypoint Detection"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[{"slug":"tampar","name":"TAMPAR","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}