<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1805.12185/</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/1805.12185/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks — mentioned by ianbutler</title><link>https://news.ycombinator.com/item?id=35807568</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.12185:35807568</guid><pubDate>Wed, 03 May 2023 20:47:44 GMT</pubDate><description>&lt;p&gt;I believe you are correct, I worked on a summer research project at NYU in 2018 based on https://arxiv.org/abs/1805.12185 As part of that project I constructed an API that took a small dataset and a model, launched a K8s pod and ran something like this from the paper: &amp;gt; The pruning defense works as follows: the defender exercises the DNN received from the attacker with clean inputs from the validation dataset, D_valid, and records the average activation of each neuron. The defender then iteratively prunes neurons from the DNN in increasing order of average activations and records the accuracy…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.12185/"&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>
