{"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/clonability-of-anti-counterfeiting-printable","title":"Clonability of anti-counterfeiting printable graphical codes: a machine learning approach","arxiv_id":"1903.07359","date":"2019-03-18","proceeding":null,"authors":["Olga Taran","Slavi Bonev","Slava Voloshynovskiy"],"abstract":"In recent years, printable graphical codes have attracted a lot of attention\nenabling a link between the physical and digital worlds, which is of great\ninterest for the IoT and brand protection applications. The security of\nprintable codes in terms of their reproducibility by unauthorized parties or\nclonability is largely unexplored. In this paper, we try to investigate the\nclonability of printable graphical codes from a machine learning perspective.\nThe proposed framework is based on a simple system composed of fully connected\nneural network layers. The results obtained on real codes printed by several\nprinters demonstrate a possibility to accurately estimate digital codes from\ntheir printed counterparts in certain cases. This provides a new insight on\nscenarios, where printable graphical codes can be accurately cloned.","url_abs":"http://arxiv.org/abs/1903.07359v1","url_pdf":"http://arxiv.org/pdf/1903.07359v1.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":"clonability-of-anti-counterfeiting-printable","repo_url":"https://github.com/taranO/clonability-of-printable-graphical-codes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[{"slug":"http-sip-unige-ch-projects-snf-it-dis","name":"DP0E","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}