{"id":8368,"date":"2026-11-11T11:35:00","date_gmt":"2026-11-11T10:35:00","guid":{"rendered":"https:\/\/viva.racunalniske-novice.com\/kako-ui-bere-besedilo-prek-elektromagnetnega-sevanja-kablov-hdmi\/"},"modified":"2026-11-11T11:35:00","modified_gmt":"2026-11-11T10:35:00","slug":"kako-ui-bere-besedilo-prek-elektromagnetnega-sevanja-kablov-hdmi","status":"publish","type":"post","link":"https:\/\/viva.racunalniske-novice.com\/fr\/comment-linterface-utilisateur-peut-elle-lire-du-texte-via-le-rayonnement-electromagnetique-des-cables-hdmi\/","title":{"rendered":"Comment l&#039;IA lit le texte via le rayonnement \u00e9lectromagn\u00e9tique des c\u00e2bles HDMI"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Cette nouvelle approche r\u00e9sout un probl\u00e8me de longue date li\u00e9 \u00e0 la capture de signaux vid\u00e9o num\u00e9riques, jug\u00e9s trop complexes pour \u00eatre reconstruits de mani\u00e8re fiable en raison de leur encodage sur 10 bits et de leurs propri\u00e9t\u00e9s non lin\u00e9aires. Les chercheurs ont donc formul\u00e9 le probl\u00e8me comme une t\u00e2che inverse et d\u00e9velopp\u00e9 un mod\u00e8le d&#039;apprentissage profond bas\u00e9 sur l&#039;architecture DRUNet. Ce mod\u00e8le reconvertit les signaux \u00e9lectromagn\u00e9tiques en une image visuelle. Autre avantage important\u00a0: le syst\u00e8me ne n\u00e9cessite pas de mesures physiques complexes pour chaque configuration, car il utilise l&#039;analyse math\u00e9matique et cr\u00e9e ses propres mod\u00e8les d&#039;apprentissage.<br><br>Compar\u00e9 aux m\u00e9thodes pr\u00e9c\u00e9dentes, dont le taux d&#039;erreur pouvait atteindre 90 %, le nouveau syst\u00e8me le r\u00e9duit \u00e0 moins de 30 %. Malgr\u00e9 des progr\u00e8s significatifs, les r\u00e9sultats sont loin d&#039;\u00eatre parfaits, car le texte reconstruit contient encore un nombre important d&#039;erreurs. Ceci limite actuellement les possibilit\u00e9s d&#039;utilisation malveillante directe. La technologie est compatible avec le mat\u00e9riel radio logiciel courant et une extension pour GNU Radio. Les chercheurs ont \u00e9galement mis \u00e0 disposition le code source et une base de donn\u00e9es d&#039;environ 7 gigaoctets.<br><br>Cette d\u00e9couverte relance indubitablement le d\u00e9bat sur la s\u00e9curit\u00e9 des \u00e9crans et les fuites de donn\u00e9es \u00e9lectromagn\u00e9tiques. Pour r\u00e9duire les risques, les experts recommandent l&#039;utilisation de c\u00e2bles adapt\u00e9s, dot\u00e9s d&#039;un blindage \u00e9lectromagn\u00e9tique renforc\u00e9, d&#039;anneaux de ferrite et de bo\u00eetiers certifi\u00e9s. Les c\u00e2bles et \u00e9crans ordinaires n&#039;offrent g\u00e9n\u00e9ralement pas le m\u00eame niveau de protection contre ce type d&#039;interception.<\/p>","protected":false},"excerpt":{"rendered":"<p>Novi pristop re\u0161uje dolgoletno oviro prestrezanja digitalnih video signalov, ki so zaradi 10-bitnega kodiranja in svojih nelinearnih lastnosti veljali za preve\u010d zapletene za zanesljivo rekonstrukcijo. Raziskovalci so zato problem zastavili kot obratno nalogo in razvili model globokega u\u010denja, ki temelji na arhitekturi DRUNet. Ta elektromagnetne signale pretvarja nazaj v vizualno podobo. Pomembna prednost sistema je [&hellip;]<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[4],"tags":[192],"class_list":["post-8368","post","type-post","status-publish","format-standard","hentry","category-racunalnistvo-telefonija","tag-umetna-inteligenca"],"acf":{"subtitle":"Raziskovalci z univerze v Montevideu so razvili sistem Deep-TEMPEST, ki s pomo\u010djo umetne inteligence sestavi vsebino zaslona na podlagi elektromagnetnega sevanja kablov HDMI. Nova metoda sicer temelji na konceptu, znanem \u017ee iz \u010dasov analogne tehnologije VGA, vendar ga uspe\u0161no prena\u0161a tudi v digitalno okolje.","heading":"","summary":"Sistem Deep-TEMPEST omogo\u010da zajem besedila z zaslona prek elektromagnetnih emisij HDMI kablov s pomo\u010djo umetne inteligence, pri \u010demer stopnja napak ostaja pod 30 odstotki, re\u0161itev pa zahteva stro\u017ejo za\u0161\u010dito kablov.","thumbnail_small":"https:\/\/racunalniske-novice.com\/wp-content\/uploads\/2026\/11\/pexels-kampus-8353774-560x315.jpg","thumbnail_large":"https:\/\/racunalniske-novice.com\/wp-content\/uploads\/2026\/11\/pexels-kampus-8353774-1024x684.jpg","thumbnail_caption":"Foto: Pexels","gallery":"","video_gallery":null,"author":"","links":null,"sources":[{"title":"hwupgrade","url":"https:\/\/www.hwupgrade.it\/news\/sicurezza-software\/spiare-un-monitor-dal-cavo-hdmi-un-ia-azzecca-oltre-7-caratteri-su-10_161831.html"}],"skip_language":[]},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Kako UI bere besedilo prek elektromagnetnega sevanja kablov HDMI - Ra\u010dunalni\u0161ke novice<\/title>\n<meta name=\"description\" content=\"Sistem Deep-TEMPEST omogo\u010da zajem besedila z zaslona prek elektromagnetnih emisij HDMI kablov s pomo\u010djo umetne inteligence, pri \u010demer stopnja napak ostaja pod 30 odstotki, re\u0161itev pa zahteva stro\u017ejo z\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/viva.racunalniske-novice.com\/fr\/wp-json\/wp\/v2\/posts\/8368\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kako UI bere besedilo prek elektromagnetnega sevanja kablov HDMI - Ra\u010dunalni\u0161ke novice\" \/>\n<meta property=\"og:description\" content=\"Novi pristop re\u0161uje dolgoletno oviro prestrezanja digitalnih video signalov, ki so zaradi 10-bitnega kodiranja in svojih nelinearnih lastnosti veljali za preve\u010d zapletene za zanesljivo rekonstrukcijo. Raziskovalci so zato problem zastavili kot obratno nalogo in razvili model globokega u\u010denja, ki temelji na arhitekturi DRUNet. Ta elektromagnetne signale pretvarja nazaj v vizualno podobo. 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