id say the security from law enforcement is one half of the threat model, but graphenos is also about protecting me from google selling my data for a dollar.

maybe the source of those weird dc gas stations with cheap gas?

this guy's a strawberry bender.

can tell they need more math classes because they have all those cowlicks

Feel like it may not be if it's pointed straight up at all times. but if the plane is doing an aileron roll it would not take minimal damage, so star fox would be effected.

just from my reading of papers, optimized patterns make use of the fact that detectors (even modern transformer based architectures) are capital S Sensitive to the sillouette of an object.

in my other comment i linked a paper where they fool modern detectors with 80+% success rate. the clothing looks like a tiedye shirt to me. not fashionable, but not drawing attention either. if there is a way that you can make this fashionable is not a question i can answer :p

from my reading combining detectors (ensembling) does not help. this leaves spies with a problem, how to detect these noisy people without losing performance on normal nonnoisy people. there are tricks to this, but they are limited at best, for the same reason the noise worked in the first place. here is the reason.

assume noise makers create noise with methods A,B,C. you train on a dataset with images from the different noise makers (you don't know which method they used). each of these noisy groups will have a distict sillouette you can detect, but together, you are building a function like:

pineapple: fruit bannana: fruit apple: fruit

this is a solvable problem for ml, so how do the noise makers win ultimately? randomization in the process. if each noise is distinct there is no training, you only have one image per type of fruit, you might as well use traditional CV to detect these noisy people (good luck).

My opinion is i want a product where the customer provides a random seed which generates the process of noise generation for their shirt. the product is the transparency into how the seed effected the noise making, and an evaluation of the noise on modern off the shelf detectors.

obviously we don't see that here, but it seems doable, maybe a business idea for me? haha

this is the most recent arxiv paper on adversarial clothing (since a lot of people are speculating about if it's possible): [2511.16020] Physically Realistic Sequence-Level Adversarial Clothing for Robust Human-Detection Evasion https://share.google/VMhtGB8P2kTMorGx6

they boast an 80%+ success rate at evading the instance level (each time you walk past an ai enabled camera) detection with sequential models (more difficult to fool as they see more of your sillouette).

not saying they are fashionable and obviously aren't commercially available yet, but it definitely seems possible.

my main complaint is i wish these companies could make a good baseline so we could compare their efficacy. evaluating the trade off with the level of drip.

look at Mister 7 minutes over here. the Olympics called they want their stamina back

i keep putting books in front of my girlfriend like a moonstone, but it never works.

military contractors go brrr

I do get that this is the real differentiator to businesses in a chat app. how much it spies on employees. we need to unionize yesterday in tech

yeah dude, fuck you OP