That's not removing blur, that's making a face (out of millions) that matches the same pixellization. There's no telling what the original face was, and it's disingenuous that they don't show you the original photo.

At the end of the video they posted[1], they show the original photos of the authors, the downscaled inputs, and the outputs.

[1] https://twitter.com/ak92501/status/1267609090689323008

And, IMHO they don't look like the same person any more, at all.

Ah, thanks, I missed that in the Tweet.

They have a sandbox you can run the code yourself--I don't think we're at dystopian surveillance level just yet https://imgur.com/a/IfdLWau

You could, however, probably tile the downscale "rainbow table" in a way that would let you predict some degree of novel original from a sufficient number of tile samples.

Thing about downscale blur is that it's nearest-neighborish, so can be addressed with divide+conquer as blur effects stay local. You'd end up with a fairly large combination of potential tiles. Some wouldn't be viable faces, but we have classifiers for that already.

Entire combination trees can be culled that way to make the problem radically smaller, as long as you know it's supposed to be a face, so I don't know how hard it would really be. It's possibly pretty easy to come up with the N possible original faces with enough certainty to then match with potential targets of interest and make N small enough to use.

Isn't that exactly what the paper is doing?

I only made it through the abstract but it looks like they’re matching the entire given LR image to a known entire HR image.

I’m saying with enough data you could potentially create a more predictive “magic sharpening” algorithm that didn’t strive to match a known original picture, but instead used that matching on divide & conquer subtiles of the original LR image against of a rainbow table of reduced HR tiles to predict a set of plausible HR images.

Basically if you can figure out with whatever context you have that the 4x4 brown smudge is very likely a brown cat, you can replace it with a brown cat. And if you know that, the orange/white/black smudge next to it is probably a calico, so stitch it in.

Of course the source image would have to be bigger than this, so it couldn’t be CSI-enhance icon to landscape, really more like a really good AI upscaler. You’d need a strong way to identify plausible scenes too. We can generate novel faces now, think this fuses the two concepts.

The resolutions on those are way higher than what Signal is doing. It's not surprising that a neural network can give a decent guess at what a face can look like. Faces don't have that much entropy. But you can blur them out if you get it down to like 4x4 pixels.

Anyway, if you want scarier panopticon stuff, you should look into gait recognition, which is way harder to censor.

Besides the fact that Signals "blur" doesn't even look remotely close to your example, they're working on the phone number issue:

> PINs will also help facilitate new features like addressing that isn’t based exclusively on phone numbers, since the system address book will no longer be a viable way to maintain your network of contacts.

https://signal.org/blog/signal-pins/

I think this feature may not do as good a job of blurring faces as people expect in some cases, especially if the faces is large in the frame. I tested it and got results that were noticeably less obfuscated than the image advertised on the announcement page by Signal:

http://lelandbatey.com/projects/signal_blur_comparison/

I too tested this out immediately and noticed the surprisingly minimal blur. However, I’ve gotten another update since then and it appears to be extremely blurry now. Actually, when I first did it after the update, I thought it was just a solid color. After blurring the majority of the photo I could tell it wasn’t a solid, just extremely blurry.

What is left to prove about (Gaussian) blurring?

The intent of the blur is to hide the identity of the individual face that has been blurred. Average human sees a blurry face and assumes the person’s identity is safe. Research has repeatedly should this is false, especially when combined with other data.

Here’s another example of such research:

https://www.wired.co.uk/article/facial-recognition-systems-c...

>> “researchers said only 10 fully-visible examples of a person's face were needed to identify a blurred image with 91.5 per cent accuracy.“

A Guassian blur is not reversable, information is lost. No research shows otherwise, because it's a mathematical property of the Gaussian transform.

Some methods can be used to find one of many solutions to the blur, where certain high frequency information is preferred over others because we know the end results looks like a human face, and not just any solution. But that only means you can get out many possible faces; if your reconstruction tool only gives you want it was simply over-trained.

[edit] You just updated your post. If you have tagged, unblurred photos of the face in your blurred photo, you can (as expected) constrain the end solutions further. WHat's not clear to me from the paper is whether or not the blurred face was tagged as well. Scenario S3 seems most likely the type of scenario encountered in surveillance programs, where the results are nowhere near 91% accurate.

> A Gaussian blur is not reversable

This might be narrowly true (it’s hard to recover precisely the original image), but is not really an accurate summary in this context, if the only goal of reversal here is to recognize the face. Deconvolution will quite effectively undo gaussian blur. https://en.wikipedia.org/wiki/Deconvolution https://en.wikipedia.org/wiki/Richardson–Lucy_deconvolution https://en.wikipedia.org/wiki/Blind_deconvolution

In Photoshop, the deconvolution tool is called “Smart Sharpen”, and has a preset for a gaussian PSF.

Ironically I think a Gaussian blur is one of the few transforms that should be totally reversible. Since the Fourier transform of a Gaussian kernel is also Gaussian, it is nonzero everywhere, meaning you can in theory just divide the Fourier transform of the image by the Fourier transform of the kernel to get the original back :)

The quantization to the image colorspace and depth is probably the limiting factor, moreso if dithering is used.

As the resolution increases, the ability to reconstruct a lower resolution image goes up as well, which will be more than enough for most identification purposes.

Security as an accidental quality of a system is not security.

Fair point. Elsewhere in thread it looks like they are using a fixed resolution. I guess at that point it comes down to whether they've left enough mid-frequency content for an algorithm to identify the slightly darker/lighter patches of facial features, and whether that spacing can actually identify a person.

Wait: informations is lost if the blur is truly a gaussian process. The simulation of blur by means of a convolution can perfectly well be reversible.

Image blur is not a gaussian process.

Are you saying that convolution with a gaussian kernel is not real gaussian blur?

I'm legitimately asking. I'm really ignorant about this subject.

In theory it is, in reality there is discretization of the signal and noise

No: it is a simulation, because it is a discretization and the map can be injective (or “almost so”).

Gaussian blurring does not seem to lose enough information.

A hard 3×3 pixelization would be much more reliable, if less æsthetically pleasing.

You could make it aesthetically pleasing. Just make sure the data is boiled down to just a handful of bytes first and there won’t be anyway to reverse it.

Just blur the pixelization and it will look similar to the human eye, but with guaranteed information loss. Note that you should also discretize the colors of the pixels, which otherwise have 24 bits of information each; and maybe 2x2 pixelization might be a better idea.

Inverse problems are ill-posed, you cannot just invert the kernel, information is lost.