And even then, I'm a little suspicious of how close some of the images got to original without being given color information.
It appears that info was either hidden in the original in a way not apparent to humans or was implicit in their data set in some way that would make it fail on photos of people with different skin tones.
> the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner.
My interpretation is that they select training data by hand and generate a bunch of outputs. Repeating the process until they like the final result. From the paper:
> we allow a user to have an arbitrarily-fine level of control through on-the-fly editing of the exemplar set (E.g., “resynthesize an image using the eye from this image and the nose from that one”).
I would argue that this is a form of enhancement though, and in some cases will be enough to completely reconstruct the original image. For example, if I give you a scanned PDF, and you know for a fact that it was size 12 black Ariel text on a white background, this can feasibly let you reconstruct the original image perfectly. The 'prior' that has been encoded by the model from the large amount of other images increases the mutual information between grainy image and high-res. The catch is that uncertainty cannot be removed entirely, and you need to know that the target image comes from roughly the same distribution as the training set. But knowing this gives you information that is not encoded in the pixels themselves, so you can't necessarily argue that some enhancement is impossible. For example with celebrity images, if the model is able to figure out who is in the picture, this massively decreases the set of plausible outputs.
http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...
Enhancing some things incorrectly would be worse than leaving them ambiguous.
When humans think about "enhance", they imagine extracting subtle details that were not obvious from the original, which implies that they know very little about what distribution the original image comes from. If they did, they wouldn't have a need for "enhance" 99% of the time -- the remaining 1% is for artistic purposes, which this is indeed suited for.
It'll be interesting to see how society copes with the removal of the "photographs = evidence" prior.
> when enhancing celebrity images, if the model is able to figure out who is in the picture this massively decreases the set of plausible outputs.
This is an excellent insight.
"Zoom! Enhance! Zoom! Enhance! Enhance! Oh my god it's full of Smurfs..."
A good example is with compression algorithms for media. They work because the sound or image is predictable. And they are ineffective when the input becomes more unpredictable. But if the output is all you have then running the decompression will probably be better than just reading the raw compressed data. But you have to be aware of the limitations.
I love this cliché. I've seen it thousands of times, and probably written it myself a few times. We all repeat stuff like that ad nauseam, without ever thinking.
Because it's fundamentally flawed, especially in the context that it has usually been applied to, namely criticising the CSI:XYZ trope of "enhancing images".
The truth is that there is a lot more information in a low-res image than meets the eye.
Even if you can't read the letters on a license plate, it can be recovered by an algorithm. If the Empire State Building is in the background, it's likely to be a US license plate. Maybe only some letters would result in the photo's low-res pattern. If you only see part of a letter, knowing the font may allow you to rule out many letters or numbers etc...
It's similar to that guy who used Photoshop's swirl effect to hide his face, not knowing that the effect is deterministic, and can easily be undone.
The error mostly appears to be in assuming that the information has been destroyed, when in reality it's often just obscured. And Neural Nets are excellent in squeezing all the information out noisy data.
The effect does not only need to be deterministic, but also invertible.
A low-res image has multiple "inverses" (yikes), supposedly each with an associated probability (if you would model it that way). So it would be more honest if the algorithm shows them all.
> I love this cliché. I've seen it thousands of times, and probably written it myself a few times. We all repeat stuff like that ad nauseam, without ever thinking.
It is not a cliche it is an absolute truth. Information not present cannot be retrieved. There may be more information present than is immediately obvious.
> Neural Nets are excellent in squeezing all the information out noisy data
Maybe but they are also good at overfitting onto noisy data (the original article is an example of such overfitting).
Yes, a low-res image has lots of information. You can process that information in many ways. Missing data can't just be magically blinked into existence though.
Copy/pasting bits of guessed data is NOT getting back information that has been destroyed or never captured. Obscured data is very different from non-existent data. Could the software recreate a destroyed painting of mine based on a simple sketch? Of course not, because it would have to invent details it knows nothing about.
I think it's almost dangerous to call this line of thinking cliché. It should be celebrated, not ridiculed.
https://photo.stackexchange.com/questions/17098/csi-image-re...
Somewhat no, but somewhat yes. Thing is, while there can be lots of input images that generate the same output, it could be that only one (or a handful) of them would occur in reality. If this happens to sometimes be the case, and if you could somehow guarantee this was the case in some particular scenario, it could very well make sense to admit it as evidence. Of course, the issue is that figuring this out may not be possible...
But that's not fully accurate either. Sometimes the information in total will really be a more accurate representation of reality than the blurred image. Maybe it could be described as an educated guess, sometimes wrong, sometimes invaluable.
It would be interesting to see the results starting with higher quality images. With the camera quality increasing, many times there should be more data to start with.
A
When is a guess invaluable?
[0] https://i.pinimg.com/originals/b5/29/1b/b5291bba7250abd12010...
Already being done today with DNA.
- I'm joking of course :) hehe
https://www.wired.com/2017/04/courts-using-ai-sentence-crimi...
This thread a year ago worried about it too, but the paper itself seems implausible and problematic.
Don't get me wrong, I think we're still far far far off situation where we can get those reliably, but I can see how you could get the actual face out of a blurred image.
Errr wrong. A perfect hash, yes. But they're never perfect. You have a collision domain and you hope that you don't have enough inputs to trigger a birthday paradox.
Look at the pictures on the article. It's an outline of the shoe. That's your hash. ANY shoe with that general outline resolves to that same hash.
If your input is objects found in the Oxford English Dictionary, you'll have low collisions. An elephant doesn't hash to that outline. But if your inputs is the Kohl's catalog, you'll have an unacceptable collision rate.
Hashes are attempts at creating a _truncated_ "unique" representation of an input. They throw away data they hope isn't necessary to uniquely identify between possible inputs (bits). A perfect hash for all possible 32 bit values is 32 bits. You can't even have a collision free 31 bit hash.
So back to the blurry security camera footage of a license plate or a face. Sure, that "hash" can reliably tell you that it wasn't a sasquatch that committed the robbery, but it literally doesn't contain the data necessary to _ever_ prove it was the suspect in question, even if the techs _can_ prove that the suspect hashes to the image in the footage.
Crafting a perfect hash function with keys being the set of words from the OED is perfectly reasonable. It’ll take a short while to produce it, but it’ll work just fine. (rust-phf says that it “can generate a 100,000 entry map in roughly .4 seconds when compiling with optimizations”, and the OED word count is in the hundreds of thousands.)
Thanks for the rust-phf link. I'm bookmarking for my next project!
For a face, sure, for printed text/license plates there are effective deblurring algorithms that in some cases may rebuild a readable image.
A (IMHO good) software is this one (was freeware, now it is Commercial, this is the last freeware version):
https://github.com/Y-Vladimir/SmartDeblur/downloads
You can try it (just for the fun of it) on these two images:
https://articles.forensicfocus.com/2014/10/08/can-you-get-th...
https://forensicfocus.files.wordpress.com/2014/09/out-of-foc...
https://forensicfocus.files.wordpress.com/2014/09/moving-car...
For the first choose "Out of Focus Blur" and play with the values, you should get a decent image at roughly Radius 8, Smooth 40%, Correction Strength 0%, Edge Feather 10%
For the second choose "motion Blur" and play with the values, you should get a decent image at roughly Length 14, Angle 34, Smooth 50%,
If you want ease collision examples you can take a look at people using CRC32 as hashes/digests. It is notoriously prone to collisions (since only 32 bits).
You can't compress a file by repeatedly storing a series of hashes, then hashes of those hashes, down into smaller and smaller representations. The reason that you cannot do this is that you cannot create a lossless file smaller than the original entropy. If you could happen to do so, however, you would get down to ever smaller files, until you had one byte left. But, you could never decompress such a file, because there is no single correct interpretation of such a decompression. In other words, your decompression is not the original file.
But in real life there's collisions.
And in real life image or sound compression, blurs, artifacts and resolutions, it is fundamentally destroying information in practice. It is no longer the comparatively difficult but theoretically possible task of reversing a perfect hash, but more like mapping a name to the characters/bucket RXXHXXXX where x could be anything.
There are lots of values we can replace X with which are plausible, but without an outside source of information, we can't know what the real values in the original name was.
You fundamentally can't get back information that has been destroyed/or never captured in the first place.
What you can do is fill in the gaps/information with plausible values.
I don't know whether this sounds like I'm splitting hairs, but it's really important that the general public not think we're extracting information in these procedures, we're interpolating or projecting information that is not there.
Very useful for artificially generating skins for each shoe on a shoe rack in a computer game or simulation, potentially disastrous if the general public starts to think it's applicable to security camera footage or admissible as evidence...