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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adversarial Perturbations Against Deep Neural Networks for Malware Classification · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1606.04435/</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/1606.04435/feed.xml" rel="self" type="application/rss+xml" /><item><title>Adversarial Perturbations Against Deep Neural Networks for Malware Classification — mentioned by obastani</title><link>https://news.ycombinator.com/item?id=12753611</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.04435:12753611</guid><pubDate>Thu, 20 Oct 2016 16:13:05 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s not at all the same -- self-driving cars can risk the lives not only of the passengers, but of innocent bystanders. Several commenters have pointed out problems such as rain, snow, darkness, etc., but there are more fundamental problems. For example, self-driving cars based on machine learning may be susceptible to adversarial attacks that cause the car to behave unpredictably [1,2]. How will self-driving cars know how to react when things go wrong, e.g., the stop light is broken? What about construction? I&amp;#x27;m not saying humans react perfectly in these situations, but self-driving cars…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.04435/"&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>
