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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Limitations of Deep Learning in Adversarial Settings · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.07528/</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/1511.07528/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Limitations of Deep Learning in Adversarial Settings — mentioned by ericpauley</title><link>https://news.ycombinator.com/item?id=19773290</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.07528:19773290</guid><pubDate>Sun, 28 Apr 2019 19:52:37 GMT</pubDate><description>&lt;p&gt;This is actually one of the core goals of adversarial machine learning: crafting inputs that trick a machine but look indestinguishable to a human [1]. [1] https://arxiv.org/pdf/1511.07528.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.07528/"&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>
