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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Are adversarial examples inevitable? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1809.02104/</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/1809.02104/feed.xml" rel="self" type="application/rss+xml" /><item><title>Are adversarial examples inevitable? — mentioned by laura_g</title><link>https://news.ycombinator.com/item?id=29381394</link><guid isPermaLink="false">urn:hackerpapers:mention:1809.02104:29381394</guid><pubDate>Mon, 29 Nov 2021 16:04:39 GMT</pubDate><description>&lt;p&gt;The literature has pretty consistently shown that adversarial examples can be found with only black box access (even with truncated prediction vectors), robustness methods are primarily a cat-and-mouse game between attackers and defenders, and the existence of adversarial examples is likely inevitable (https://arxiv.org/pdf/1809.02104.pdf). The big question that remains is - so what? There&amp;#x27;s exceedingly few use cases where the existence of adversarial examples causes a security threat. There&amp;#x27;s a lot of research value in understanding adversarial examples and what that tells us about how…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1809.02104/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Are adversarial examples inevitable? — mentioned by rademacher</title><link>https://news.ycombinator.com/item?id=18396273</link><guid isPermaLink="false">urn:hackerpapers:mention:1809.02104:18396273</guid><pubDate>Wed, 07 Nov 2018 00:48:18 GMT</pubDate><description>&lt;p&gt;This latest craze of &amp;quot;AI&amp;quot; research seems to be fueled by a sudden glut of computational power (GPUs) that wasn&amp;#x27;t available previously. I think that most technical people would agree that the mid 2020s is extremely ambitious. I&amp;#x27;d also argue that we&amp;#x27;re actually more likely to experience another AI winter. The frightening part of the current deep learning research is how susceptible they are to adversarial attacks. Adding small amounts of noise causes misclassification in images, and some papers even explore the inevitability of adversarial examples [1]. This is especially frightening given the…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1809.02104/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
