{"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/the-use-of-electric-network-frequency","title":"The use of Electric Network Frequency presence in video material for time estimation","arxiv_id":null,"date":"2022-02-24","proceeding":"Journal of forensic sciences 2022 2","authors":["Guus Frijters","Zeno J. M. H. Geradts"],"abstract":"In this research, the possibility of estimating the time a video was recorded at through electric network frequency is explored by examining various light sources in differentiating circumstances. This research focuses on videos made with smartphones. The smartphone cameras make use of an integrated complementary metal oxide semiconductor sensor. The filmed videos are analyzed using software, which employs a small electric network frequency (ENF) database to determine the time of recording of a video made in experimental circumstances. This research shows that in ideal circumstances, it is possible to determine the time stamp of a video recording made with a smartphone. However, it becomes clear that different light sources greatly influence the outcome. The best results are achieved with Halogen and Incandescent light sources, both of which also seem promising in less ideal circumstances. LED sources do work in ideal circumstances and, however, do not show much success in lesser circumstances. This research further demonstrates that there is potential in using ENF to determine a time stamp of recorded videos and provides validation on prior research on this topic. It proves usable in ideal circumstances with the presence of a clear light source on a white wall. With additional research, it has potential to become a feasible method to use for forensic settings in circumstances that are less ideal.","url_abs":"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.15003","url_pdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1556-4029.15003","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":"the-use-of-electric-network-frequency","repo_url":"https://github.com/GuusF16/ENF_extraction_from_video","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"enf-electric-network-frequency-extraction","task_name":"ENF (Electric Network Frequency) Extraction"},{"task_slug":"enf-electric-network-frequency-extraction-1","task_name":"ENF (Electric Network Frequency) Extraction from Video"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}