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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Test-Time Poisoning Attacks Against Test-Time Adaptation Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2308.08505/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2308.08505/feed.xml" rel="self" type="application/rss+xml" /><item><title>Test-Time Poisoning Attacks Against Test-Time Adaptation Models — mentioned by amarcheschi</title><link>https://news.ycombinator.com/item?id=49757479</link><guid isPermaLink="false">urn:hackerpapers:mention:2308.08505:49757479</guid><pubDate>Fri, 18 Sep 2026 17:24:35 GMT</pubDate><description>&lt;p&gt;Ok sorry but I forgot to read if there were answers. So I still have to do things concretely and have moved to another country, for now I have mostly read papers (...). But well anyway There&amp;#x27;s a class of ml algorithms that belong to continuous learning, what they do is integrate knowledge as they operate. In short, the difference between a traditional ml is that once it&amp;#x27;s trained the weights don&amp;#x27;t change, while here they do. The thing is, there are a handful of techniques that you can used, based on your scenario, to degrade the performance of these models. In a white box attack you assume…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2308.08505/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item></channel></rss>
