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None of Your Pixelated or Blurred Information Will Stay Safe On The Internet (qz.com)
The University of Texas at Austin and Cornell University are saying blurred or pixelated images are not as safe as they may seem. As machine learning technology improves, the methods used to hide sensitive information become less secure. Quartz reports: Using simple deep learning tools, the three-person team was able to identify obfuscated faces and numbers with alarming accuracy. On an industry standard dataset where humans had 0.19% chance of identifying a face, the algorithm had 71% accuracy (or 83% if allowed to guess five times). The algorithm doesn't produce a deblurred image -- it simply identifies what it sees in the obscured photo, based on information it already knows. The approach works with blurred and pixelated images, as well as P3, a type of JPEG encryption pitched as a secure way to hide information. The attack uses Torch (an open-source deep learning library), Torch templates for neural networks, and standard open-source data. To build the attacks that identified faces in YouTube videos, researchers took publicly-available pictures and blurred the faces with YouTube's video tool. They then fed the algorithm both sets of images, so it could learn how to correlate blur patterns to the unobscured faces. When given different images of the same people, the algorithm could determine their identity with 57% accuracy, or 85% percent when given five chances. The report mentions Max Planck Institute's work on identifying people in blurred Facebook photos. The difference between the two research is that UT and Cornell's research is much more simple, and "shows how weak these privacy methods really are."
Why would you show a blurred photo anyway? Show the face in full, or don't show it at all. There is no compromise here.
It gets worse when they only cover the eyes. If that was effective, then sunglasses would be treated the same as balaclavas.
is an interface that accepts voice commands.
ENHANCE!
This is my shocked face: https://upload.wikimedia.org/wikipedia/en/d/d8/Mr_Swirl.jpg
Putting a big black square over your face in Paint is the only surefire method.
Anyway, using social media and simultaneously demanding privacy is pure silliness to begin with. The real question is how much of an illusion of privacy needs to be maintained to keep you from complaining.
I felt a great disturbance in the force, as if a million Japanese porn fans cried out in disappointment.
Confucius say, "Find worm in apple - bad. Find half a worm - worse."
Unless I'm reading it wrongly they already have a potential facial match for the machine to compare against. How well would it do given a single blurred face and the entire drivers liscense database of faces to match it against?
For a computer, most algorithms behind comparing two pictures is already a blurred picture of both. Most of these algorithms take samples/pixels of the pictures and see if the relationships of both sets of samples are the same or within a margin of deviation. There is little value in comparing pixel by pixel for exact matches. Similar to human finger prints.
A blurred picture is similar to taking less samples on one picture and setting the margin of deviation wider.
But for computers, 57% is pretty bad. 85% is also very bad and that's when you are telling the machine the answer. At those rates, this is kind of hard to do mass comparisons... the false positives would be far too high for any human to weed through. This will apply more for targeted searches where an investigator wants the 5 most probable matches to a blur. Unlike the researchers here who know the answer before hand, he still needs to take the guess on which one it actually is.
In a criminal investigation, if we had a database of likely suspects, this would work. But we are all about mass collection of data data data. With a large population of pictures, the blur will probably match a lot more than 5.
Just do like I do. If you put a picture of your car online, put a black bar over the license plate.
Have you ever fallen asleep at the keybhanusdiog?
Blurring is a technique reversible with Wiener filtering. Basically the quality of recovery very much depends on subsequent quantization/compression. Pixelation is more complete information loss.
However, the article talks about video filters. In that case, per-frame pixelization will let a lot of image detail become recoverable through motion compensation (straightforward blurring is less suscetible to this recovery strategy). So if you really want to inhibit recovery, blot out the information hard. The less your result depends on the originally available information, the better.
And people can still be identified by gait etc, so just blocking out the face is still not buying you perfect cover.
Pixelated too
This technique only works when the computer has a copy of the exact same picture (unblurred) in its database.
Would it work if you took a different picture of the same person? Changed the lighting a bit, or altered the angle of the head. No, it wouldn't.
They used pictures of 40 distinctly different faces (10 pictures per face). That dataset is way too small to say anyting usefull about the systems capability to recognize blurred faces.
In any larger dataset, i.e. real world applications, the recognition rate would plummet even lower than it already is, and the number of false positive would skyrocket as well.
That means, give it a million faces, give it a blurred picture, and it will come up with at least a hundred-thousand potentials, all with about equal confidence level.
It is a fundamental law of computer science that you cannot increase the amount of information in a given dataset. In this case the combined dataset of the blurred image and the learned statistical averages of a human face.
Once an image has been blurred (information has been deleted) it cannot be recreated. What you can do is to apply statistical averages in the hopes of getting something which might resemble the original information. It will - however - be just that, cosmetic improvements based on statistical averages.
If sufficient information has been removed by blurring the image, the deblurring process - no matter if you use the word AI or statistic averages - cannot recreate a uniquely identifiable image.
But can it see the blurred nipples? That's the important question!
just black it out entirely, and remember that jpeg images may retain thumbnails, from which the original image can be extracted.
Being stuck with blurred and pixelated text for decades, we old folks have developed an extra sense for that. Damn the display manufacturers. Sorry, I'm so tired of low resolution a.k.a high definition displays, I just needed to get rid of this rage.
You know, you raise a good question.
I've just always been told it's a good practice, but yeah, what would someone actually be able to do with someone else's plate numbers?
Have you ever fallen asleep at the keybhanusdiog?
Can we stop with this "deep learning" bullshit now? It is just algorithms. Every moron has to interject "AI" or "deep learning" or "neural nets" into their program description. This is really stupid "research" anyway. Is this what passes for research in CS now?
I remember a demo of Photoshop from two years ago when they managed to reverse lens blur off a photo. It was only a matter of time.
They had a photo with an obscured face and the same photo with unobscured face in their training set. It seems obvious a computer can match those two. The solution would be to use unique photos, not uploaded anywhere, as the source for obscuration and only publish the obscured version.
I'd just write a GUI interface using Visual Basic to track an IP address of the pixilated person.
Most Asian women I see in the US are no longer pixelated.
"That's the way to do it" - Punch
Surprisingly he's not dead yet
https://en.wikipedia.org/wiki/...
news report
http://thelede.blogs.nytimes.c...
etc
"It is a greater offense to steal men's labor, than their clothes"
So it's good that I always paste a different image over the area I pixelate... Good to know
Photo to photo matching was a largely solved problem a decade ago. We did this at work back then but didn't really pursue it as Tineye went public with its version and good marketing effort just after we had successfully implemented our system.
If only we had this tech for watching blurry cable TV porn in the 80s and 90s. Live would have been awesome!
Why would you show a blurred photo anyway? Show the face in full, or don't show it at all. There is no compromise here.
That's no image filter, that's just the way my face naturally looks, you insensitive clod!
If you care about security, you shouldn't be using blur, you should be putting a nice black circle over it. A yellow smiley face works just as well.
Long timers will remember all the PAID NSA articles on Slashdot warning peeps not to bother properly deleting their hard-drive files cos 'magic' NSA tech could scan the magnetic surface of the platters and recover them. This FUD is DESIGNED to discourage the easily influenced (ie., ANYONE who listens to mainstream media news outlets) from using best security practices.
Notive how Hitlery Clinton used a FREE open source security tool to delete her files- knowing that ALL commercial applications are NSA compromised/back-doored?
Just as there are PERFECT and terrible ways of deleting your files, there are PERFECT and terrible 'blur' filters to choose from. Commercial 'security' blur filters will ALWAYS be sold cos the NSA can usually apply a good enough reverse transorm.
Truecrypt STILL encrypts your files so no-one on the planet can brute force decode them, which is why the NSA spent BILLIONS destroying the Truecrypt project- and FUDed it to 'death' in the eyes of most sheeple.
If Slashdo pushes a thing, look for the agenda- there will ALWAYS be one. The ONLY best practice security comes from using free software with a provable mathematical premise behind it. Non-revesable 'blur' transforms (which strictly speaking aren't really 'blurs') have been known and understood for years.
If I take a photo that has license plates, street addresses etc...I CUT them out, PAST another image of the photo in place. If you don't just blur it, but REMOVE it, how would they figure it out?
It's gone.
Between social media mining, NSA/CIA/FBI operations, license plate readers, Stingray gadgets, the GPS in your phone, cell tower triangulation, TPMS scanners, and the video cams on every power pole and stop light, the concept of 'privacy' or anonymous behavior is pretty much gone.
I'd wager it would be nearly impossible to travel between any to major cities or buy anything in a store without leaving a trackable signature.
You'd basically have to travel by bicycle with your head covered (leaving your cell phone at home, of course) and pay for stuff in cash while wearing gloves. I'm not sure even that would do it.
Just cruising through this digital world at 33 1/3 rpm...
Wait... you mean that's an actual thing now?
None of Your Information Will Stay Safe On The Internet.
there's an easier way
Would using multiple blur type filters more than once help?
Say you do a pixelate and then a gaussian blur and then several more cycles of that with different parameters.
An easy way to defeat this is 2-step:
'Explode' a selection. (Randomly moves every pixel based on X) / or add random noise.
Pixelate that.
This will horribly mangle faces, text and anything where detail is important.
Well, unless your pixelate algo blurs pixels in an XxY grid, it won't be as good.
Either mask the face/license plate/whatever entirely or replace it with a "fake blur" that was made from another image.
For license plates, use a sample plate like ABC-123 to generate the blurred image.
Faces will be a little harder to do: Either 1) you will only have a few "sample faces" and things will look creepy even if you use the best-matching sample, 2) you will have a few thousand and you will, in effect, leak information, or 3) you will be in between and it will look creepy and leak information.
Perhaps just masking the face altogether will be the best option.
Knowledge is how to play a game, intelligence is how to win, wisdom is knowing what game to play.
That's what they do on all the crime scene investigation shows!
The lens has a certain unchanging point spread function (how a point of light is spread into a blur) which scales linearly with distance away from (and closer than) the focal plane. You estimate the size of the blur, then apply an inverse of this PSF. The process is similar to tomography used in CT scans (Computer-assisted Tomography) and MRIs. Likewise, camera shake simply adds a linear smear component to this PSF. Heck, technically you don't even need a lens. The light shining through a window which falls as a smear on a piece of paper (or sensor) is just the Fourier transform of the scene visible through the window. FTs are symmetric (reversible), so if you run a FT of what that sensor records, you get back the image out the window. The catch being that you need to record both intensity and phase data. That's what a hologram does. You shine a laser at a scene, and the scattered laser light (FT'ed) falls onto the holographic film which records the light interference pattern (phase and intensity data). Shining the same laser light through the film (another FT) recreates an image of the original scene. This is also how light field cameras work, and why they're able to change focus after the "photo" has been taken. They're capturing the light field (intensity and phase), and are able to completely reconstruct the scene.
Gaussian blurs are harder to undo because they're random. "Gaussian" means you're applying a random blur which falls within a statistical normal curve. i.e. Each time you run a Gaussian blur on the same photo, the end result is slightly different. But in blurred video, you've got multiple sequential Gaussian blurs of the same subject. Time-averaging those causes the normal curve to narrow into a sharp peak, at which point the statistical randomness is close to nil and you can (theoretically) reverse the blur.
But you know who Tony and Dr. Melfi are?
Turns out someone's been fixing blurred lines for a while now
https://app.box.com/WitthoftResume Code: https://github.com/cellocgw
Let's see their 'deep learning tool' identify something that's got a featureless black box over it, or someone's face that's got a black box or oval over it.
Are YOU using the TOOL, or is the TOOL using YOU? Think about it!
All of this research, all of the effort.
Defeated by a black box in MS PAINT.
http://www.martinbackes.com/pixelhead-limited-edition/
as any fule kno