<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Series on Adversarial Network &amp; Robustness</title><link>https://xiaokunduan.github.io/tags/series/</link><description>Recent content in Series on Adversarial Network &amp; Robustness</description><generator>Hugo -- 0.159.0</generator><language>en-us</language><lastBuildDate>Sat, 13 Sep 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://xiaokunduan.github.io/tags/series/index.xml" rel="self" type="application/rss+xml"/><item><title>Adversarial Robustness, in 5 Readable Parts</title><link>https://xiaokunduan.github.io/posts/adversarial-robustness-series/</link><pubDate>Sat, 13 Sep 2025 00:00:00 +0000</pubDate><guid>https://xiaokunduan.github.io/posts/adversarial-robustness-series/</guid><description>A shorter, more readable 5-part path through adversarial robustness: motivation, theory, attacks, defenses, and high-stakes deployment.</description></item><item><title>Why AI Can Be Brilliant but Fragile</title><link>https://xiaokunduan.github.io/posts/part-1-why-ai-is-fragile/</link><pubDate>Mon, 08 Sep 2025 00:00:00 +0000</pubDate><guid>https://xiaokunduan.github.io/posts/part-1-why-ai-is-fragile/</guid><description>Why high-performing AI systems can still fail under tiny perturbations, and why that fragility matters.</description></item><item><title>What Robustness Really Means</title><link>https://xiaokunduan.github.io/posts/part-2-what-robustness-really-means/</link><pubDate>Tue, 09 Sep 2025 00:00:00 +0000</pubDate><guid>https://xiaokunduan.github.io/posts/part-2-what-robustness-really-means/</guid><description>A theory-first guide to Bayes error, gradients, and loss landscapes as the foundation for robustness.</description></item><item><title>How Adversarial Attacks Evolved</title><link>https://xiaokunduan.github.io/posts/part-3-how-attacks-evolved/</link><pubDate>Wed, 10 Sep 2025 00:00:00 +0000</pubDate><guid>https://xiaokunduan.github.io/posts/part-3-how-attacks-evolved/</guid><description>From FGSM and PGD to 3D attacks, adversarial viewpoints, and explainability attacks.</description></item><item><title>How We Defend Models Against Adversarial Attacks</title><link>https://xiaokunduan.github.io/posts/part-4-how-we-defend-models/</link><pubDate>Thu, 11 Sep 2025 00:00:00 +0000</pubDate><guid>https://xiaokunduan.github.io/posts/part-4-how-we-defend-models/</guid><description>A compact map of the main defense routes: adversarial training, data-centric methods, certification, and efficient purification.</description></item><item><title>Robustness in Modern Models and High-Stakes Settings</title><link>https://xiaokunduan.github.io/posts/part-5-robustness-in-modern-high-stakes-settings/</link><pubDate>Fri, 12 Sep 2025 00:00:00 +0000</pubDate><guid>https://xiaokunduan.github.io/posts/part-5-robustness-in-modern-high-stakes-settings/</guid><description>Why robustness becomes a systems problem in modern architectures and high-cost applications.</description></item></channel></rss>