Hacker Newsnew | past | comments | ask | show | jobs | submitlogin

I wonder if such "patterns" could find use in clothing or as bumper stickers. I can envision this sort of thing taking off as a counter-culture, or anti-technology social weapon. It certainly wouldn't be hard to produce and iterate on.

I imagine it would be hard to enforce, let alone legislate against subtle visual cues that trigger machine vision signals.

Interesting times lie ahead...



About 10 years ago, when I learned about the EURion Constellation[1], I made a T-shirt with the pattern on it. It sort-of worked - a photo taken at the right distance would frequently cause a copy machine to refuse to copy it. It was twitchy, though.

At the time, I was thinking of posing for my DMV photo with it on, because I thought it was interesting and kinda funny. Failed to do so and never resumed the experiment.

[1] https://en.wikipedia.org/wiki/EURion_constellation


People have been using a similar idea to counter facial recognition.

https://cvdazzle.com/


More recently, CMU made glasses that can make you show up as someone else. http://qz.com/823820/carnegie-mellon-made-a-special-pair-of-...


The AI can be trained to become robust to Adversarial examples. Not only that, there is work going on right now that aims to recognize all Adversarial examples. Early results are promising.

I fear for the future if the fascists control the neural networks.


Did you read the article?

> We find that both adversarial training and defensive distillation accidentally perform a kind of gradient masking. Neither algorithm was explicitly designed to perform gradient masking, but gradient masking is apparently a defense that machine learning algorithms can invent relatively easily when they are trained to defend themselves and not given specific instructions about how to do so. If we transfer adversarial examples from one model to a second model that was trained with either adversarial training or defensive distillation, the attack often succeeds, even when a direct attack on the second model would fail. This suggests that both training techniques do more to flatten out the model and remove the gradient than to make sure it classifies more points correctly.


Ah, haven't kept up with Adversarial examples research lately,sorry.


or water transfer printed "makeup"




Consider applying for YC's Fall 2026 batch! Applications are open till July 27.

Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: