<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>pandemic_region</author><text>thing is, when you walk around on the street looking like the people on their website you don&amp;#x27;t actually need surveillance camera&amp;#x27;s because every single person will remember seeing you.</text><parent_chain><item><author>pizza</author><text>CV Dazzle [0] is&amp;#x2F;was a style of makeup that defeated facial recognition, and it seemed to work from multiple angles. Unfortunately it was released something like 5 years ago so I&amp;#x27;m certain it won&amp;#x27;t withstand today&amp;#x27;s state of the art techniques. Looks like it was never really tested to work against deep learning-based techniques.&lt;p&gt;&lt;i&gt;That&lt;/i&gt; said, I think somebody could very easily come up with an adversarial makeup camouflage recommender. Imagine an interactive visualization of the regions of confidence of a facial recommendation system, hooked up to your webcam. You could have a visual overlay of where to apply make up to most damage the classifier&amp;#x27;s confidence of your identity.&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;cvdazzle.com&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;cvdazzle.com&amp;#x2F;&lt;/a&gt;</text></item><item><author>rtkwe</author><text>Figure 7 in the paper [0] shows something really important. The learned adversarial patch is not general at all and has to be held in a specific place with a specific orientation to function. This means it&amp;#x27;s not going to be generalizable to actual surveillance where you&amp;#x27;ll be viewed from a lot of different potential angles or say onto a shirt or jacket you can just wear.&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1904.08653.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1904.08653.pdf&lt;/a&gt;</text></item></parent_chain></comment><story><title>How to hide from the AI surveillance state with a color printout</title><url>https://www.technologyreview.com/f/613409/how-to-hide-from-the-ai-surveillance-state-with-a-color-printout/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>Zaphods</author><text>That CV Dazzle project is fascinating. The link to the warship Dazzle camouflage is also hilarious in the sense that that is what people have to turn to in 2019.&lt;p&gt;It reminds me of the comic book &lt;i&gt;The Private Eye&lt;/i&gt; by Brian K Vaughn. In that story the cloud &amp;quot;bursts&amp;quot; and online privacy evaporates overnight so everyone turns to intense camouflage to protect their identity. (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;The_Private_Eye&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;The_Private_Eye&lt;/a&gt;)</text><parent_chain><item><author>pizza</author><text>CV Dazzle [0] is&amp;#x2F;was a style of makeup that defeated facial recognition, and it seemed to work from multiple angles. Unfortunately it was released something like 5 years ago so I&amp;#x27;m certain it won&amp;#x27;t withstand today&amp;#x27;s state of the art techniques. Looks like it was never really tested to work against deep learning-based techniques.&lt;p&gt;&lt;i&gt;That&lt;/i&gt; said, I think somebody could very easily come up with an adversarial makeup camouflage recommender. Imagine an interactive visualization of the regions of confidence of a facial recommendation system, hooked up to your webcam. You could have a visual overlay of where to apply make up to most damage the classifier&amp;#x27;s confidence of your identity.&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;cvdazzle.com&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;cvdazzle.com&amp;#x2F;&lt;/a&gt;</text></item><item><author>rtkwe</author><text>Figure 7 in the paper [0] shows something really important. The learned adversarial patch is not general at all and has to be held in a specific place with a specific orientation to function. This means it&amp;#x27;s not going to be generalizable to actual surveillance where you&amp;#x27;ll be viewed from a lot of different potential angles or say onto a shirt or jacket you can just wear.&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1904.08653.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1904.08653.pdf&lt;/a&gt;</text></item></parent_chain></comment><story><title>How to hide from the AI surveillance state with a color printout</title><url>https://www.technologyreview.com/f/613409/how-to-hide-from-the-ai-surveillance-state-with-a-color-printout/</url></story>