CV Dazzle [0] is/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'm certain it won't withstand today's state of the art techniques. Looks like it was never really tested to work against deep learning-based techniques.
That 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's confidence of your identity.
thing is, when you walk around on the street looking like the people on their website you don't actually need surveillance camera's because every single person will remember seeing you.
I don't think that's true. I'm no expert on facial recognition, but I believe those systems look at more specific details, like the corners of your eyes and mouth, and where they are relative to your jawline. They're less interested in things like "could this be part of a cheek? does that hair cover a head? does the picture as a whole make sense?". They're very precise (more than we are) at measuring the exact locations of specific features, but less good at checking if the picture as a whole could be hiding a person.
So they're better than us at some things, but can still fail dramatically at things that seem trivial to us.
It's certainly not a way to stay stealthy, yeah. And frankly it's probably worse at defeating face recognition than wearing a bulky hoody and staring at your shoes while you walk. I don't think this will be identity-concealing except maybe at some cybergoth raves...
It's an awfully cool project, though; both artistic and a transformation-resilient adversarial input.
I guess the solution could eventually be digital paint which rapidly and randomly alters itself, something akin to the scramble suits from A Scanner Darkly. https://www.dailymotion.com/video/xqrvzb
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.
It reminds me of the comic book The Private Eye by Brian K Vaughn. In that story the cloud "bursts" and online privacy evaporates overnight so everyone turns to intense camouflage to protect their identity. (https://en.wikipedia.org/wiki/The_Private_Eye)
Does face detection today use the same kind of trained neural nets that YoLo or others use? Watching this video [0] from their site it seems it's using a much more static algorithm trying to detect particular features which is what the CV Dazzle makeup aims to disrupt. As far as I understand one of the benefits of the neural net models is that they're less reliant on defined features than the older CV models.
On the website it talks about openCV and how to fool that, as well as the general techniques used. I still think from the tips shown that it would be very effective today. For exaple, it talks about how the most distinct features of a face are the nose and eyes, and that hiding or obscuring them works well.
Newer technology likely gets much better at spotting noses and eyes, but taking them entirely out of the picture probably helps. Also the asymmetry I can imagine helps a lot.
That 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's confidence of your identity.
[0] https://cvdazzle.com/