{"id":6081,"date":"2023-10-03T17:36:17","date_gmt":"2023-10-03T17:36:17","guid":{"rendered":"https:\/\/dailyai.com\/?p=6081"},"modified":"2023-10-03T17:36:17","modified_gmt":"2023-10-03T17:36:17","slug":"ai-image-watermarks-dont-work-and-probably-never-will","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/10\/ai-image-watermarks-dont-work-and-probably-never-will\/","title":{"rendered":"AI-billedvandm\u00e6rker virker ikke og kommer nok aldrig til det"},"content":{"rendered":"<p><strong>Virksomheder fors\u00f8ger at bruge digitale vandm\u00e6rker til at identificere AI-billeder, men ny forskning viser, at deres indsats kan v\u00e6re forg\u00e6ves.<\/strong><\/p>\n<p>Et hold forskere fra University of Maryland testede avancerede billedvandm\u00e6rkningsteknikker, og resultaterne var ikke gode.<\/p>\n<p>Soheil Feizi, professor i datalogi ved University of Maryland, sagde, at hans team testede alle eksisterende vandm\u00e6rketeknikker og \u00f8delagde dem alle.<\/p>\n<p>Den <a href=\"https:\/\/arxiv.org\/abs\/2310.00076\" target=\"_blank\" rel=\"noopener\">forskningsartikel<\/a> anerkender, at vandm\u00e6rkning ser ud til at v\u00e6re det mest lovende forsvar mod dybe forfalskninger, men skitserer derefter manglerne i de nuv\u00e6rende tilgange.<\/p>\n<p>De to vigtigste metoder til vandm\u00e6rkning er lav og h\u00f8j forstyrrelse.<\/p>\n<p>Metoden med lav forstyrrelse indeb\u00e6rer en subtil \u00e6ndring af et billede, som er um\u00e6rkelig for \u00f8jet, men som stadig kan registreres digitalt. Disse metoder viste en afvejning mellem ydeevne og falske positiver. Jo bedre metoden var til at identificere en AI-falsk, jo mere sandsynligt var det, at den fejlagtigt identificerede et \u00e6gte billede som falsk.<\/p>\n<p>H\u00f8j forstyrrelse indeb\u00e6rer, at der tilf\u00f8jes et synligt element til billedet, hvilket anses for at v\u00e6re en mere robust tilgang. Forskerne var ogs\u00e5 i stand til at fjerne alle disse vandm\u00e6rker. Deres \"model substitution adversarial attack\"-metode fjernede endda de mere subtile og avancerede vandm\u00e6rker i form af tr\u00e6ringe.<\/p>\n<p>Da AI forts\u00e6tter med at skabe og sprede misinformation, har vi brug for en effektiv m\u00e5de at se, hvorn\u00e5r et billede eller en video er skabt af AI. Denne unders\u00f8gelse konkluderer dog, at en effektiv l\u00f8sning ser mere og mere usandsynlig ud, om end ikke umulig.<\/p>\n<p>Feizis team testede kun de tilg\u00e6ngelige vandm\u00e6rker. Google og Meta arbejder p\u00e5 deres egne vandm\u00e6rketeknologier, men de var ikke tilg\u00e6ngelige for test endnu.<\/p>\n<p>Alligevel mener Feizi, at deres forskning viser, at selv de store teknologivirksomheder ikke vil kunne levere en idiotsikker l\u00f8sning. A <a href=\"https:\/\/arxiv.org\/pdf\/2306.01953.pdf\" target=\"_blank\" rel=\"noopener\">artikel udgivet af forskere fra University of California<\/a> konkluderede, at \"alle usynlige vandm\u00e6rker er s\u00e5rbare.\"<\/p>\n<p>Efterh\u00e5nden som generativ AI bliver bedre, vil det sandsynligvis blive sv\u00e6rere at identificere et AI-genereret billede. S\u00e5 selv om kommercielle AI-billedgeneratorer som <a href=\"https:\/\/dailyai.com\/da\/2023\/08\/youtuber-reveals-leaked-images-from-openais-unreleased-dall-e-3\/\">DALL-E<\/a> eller Midjourney anvender et effektivt vandm\u00e6rke, er der ikke noget, der tvinger andre modeller til at g\u00f8re det.<\/p>\n<p>Det andet problem, der blev fremh\u00e6vet i Feizis forskning, er, at de var i stand til at indf\u00f8re AI-vandm\u00e6rker i billeder, der ikke var AI-genererede.<\/p>\n<p>Problemet med dette er, at n\u00e5r man bliver pr\u00e6senteret for fotografisk bevismateriale, kan d\u00e5rlige akt\u00f8rer anvende et AI-vandm\u00e6rke p\u00e5 det i et fors\u00f8g p\u00e5 at miskreditere bevismaterialet.<\/p>\n<p>Vi m\u00e5 se, hvordan Googles og Metas vandm\u00e6rker klarer sig mod Feizis teams brute-force-angreb. Indtil videre ser det ud til, at vi bliver n\u00f8dt til at minde os selv om, at man ikke skal tro, hvad man ser.<\/p>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>Virksomheder fors\u00f8ger at bruge digitale vandm\u00e6rker til at identificere AI-billeder, men ny forskning viser, at deres indsats kan v\u00e6re forg\u00e6ves. Et hold forskere fra University of Maryland testede avancerede billedvandm\u00e6rketeknikker, og resultaterne var ikke gode. Soheil Feizi, professor i datalogi ved University of Maryland, sagde, at hans team testede alle eksisterende vandm\u00e6rkningsteknikker og \u00f8delagde dem alle. Forskningsartiklen anerkender, at vandm\u00e6rkning ser ud til at v\u00e6re det mest lovende forsvar mod dybe forfalskninger, men skitserer derefter manglerne i de nuv\u00e6rende tilgange. De to vigtigste metoder til vandm\u00e6rkning er lav og h\u00f8j forstyrrelse. Den lave<\/p>","protected":false},"author":6,"featured_media":6084,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[94,107,165],"class_list":["post-6081","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deep-fakes","tag-generative-ai","tag-image-generation"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI image watermarks don\u2019t work, and probably never will | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/da\/2023\/10\/ai-image-watermarks-dont-work-and-probably-never-will\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI image watermarks don\u2019t work, and probably never will | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Companies are trying to use digital watermarks to identify AI images but recent research shows their efforts may be in vain. A team of researchers from the University of Maryland tested state-of-the-art image watermarking techniques and the results weren\u2019t good. Soheil Feizi, professor of computer science at the University of Maryland, said his team tested every existing watermarking technique and broke them all. The research paper acknowledges that watermarking appears to be the most promising defense against deep fakes but then outlines the deficiencies in current approaches. The two main methods of watermarking are low and high perturbation. 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