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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On Detecting Adversarial Perturbations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1702.04267/</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/1702.04267/feed.xml" rel="self" type="application/rss+xml" /><item><title>On Detecting Adversarial Perturbations — mentioned by derEitel</title><link>https://news.ycombinator.com/item?id=14383201</link><guid isPermaLink="false">urn:hackerpapers:mention:1702.04267:14383201</guid><pubDate>Sat, 20 May 2017 16:49:33 GMT</pubDate><description>&lt;p&gt;Here is a paper from Bosch in that direction, it uses a second network to classify examples as adversarials: https://arxiv.org/abs/1702.04267 Using a fail safe network is hard because adversarial examples usually have a high accuracy at a false class. So using an accuracy threshold in the main network wouldn&amp;#x27;t work. Using a network as described in the paper and then a different kind of classifier might be worth trying. But it has also been shown that adversarial examples can transfer to different kind of models (don&amp;#x27;t know if random forests have been tried as well).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1702.04267/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
