<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Unmasking DeepFakes with simple Features · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1911.00686/</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/1911.00686/feed.xml" rel="self" type="application/rss+xml" /><item><title>Unmasking DeepFakes with simple Features — mentioned by jpage</title><link>https://news.ycombinator.com/item?id=30319304</link><guid isPermaLink="false">urn:hackerpapers:mention:1911.00686:30319304</guid><pubDate>Sun, 13 Feb 2022 04:46:46 GMT</pubDate><description>&lt;p&gt;There are inexpensive models to identify[1] and reverse engineer[2] the generated or morphed images. Is a label like this feasible? Many people trust Google as authentic source, I remember a non-technical me can easily be fooled with a morphed image. [1] https://arxiv.org/pdf/1911.00686.pdf [2] https://ai.facebook.com/blog/reverse-engineering-generative-...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1911.00686/"&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>
