<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>spoofing on Media Presser</title>
    <link>https://mediapresser.com/tags/spoofing/</link>
    <description>Recent content in spoofing on Media Presser</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-us</language>
    <lastBuildDate>Fri, 17 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://mediapresser.com/tags/spoofing/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>How Biometric Technologies Can Fail: Bias, Spoofing, and Data Poisoning</title>
      <link>https://mediapresser.com/2026/04/17/how-biometric-technologies-can-fail-bias-spoofing-and-data-poisoning/</link>
      <pubDate>Fri, 17 Apr 2026 00:00:00 +0000</pubDate>
      
      <guid>https://mediapresser.com/2026/04/17/how-biometric-technologies-can-fail-bias-spoofing-and-data-poisoning/</guid>
      <description>Biometric technologies have a number of vulnerabilities that underscore the ethical concerns over their employment and could result in the failure of the technology to perform as anticipated.
Algorithmic Bias Researchers have repeatedly found that AI-trained facial recognition programs fail disproportionately when used for women and people of color, due to both the models and the data on which the programs were trained. If unaddressed, these challenges could result in system failure, potentially leading to violations of civil liberties or international humanitarian law.</description>
    </item>
    
  </channel>
</rss>
