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It's all a blur

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Re: It's all a blur

#11

Post by sebzim4500 »

oulipo2 wrote: post #9
praptak wrote: post #3

My (admittedly superficial) knowledge about blur reversibility is that an attacker may know what kind of stuff is behind the blur. I mean knowledge like "a human face, but the potential set of humans is known to the attacker" or even worse "a text, but the font is obvious from the unblurred part of the doc".

The parade is easy: just add a small amount of random noise (even not visible to the human eye) to the blurred picture, and suddenly the "blur inversion" fails spectacularly

Does this actually work? I would have thought that, given the deconvolution step is just a linear operator with reasonable coefficients, adding a small amount of noise to the blurred image would just add similarly small amount of noise to the unblurred result.

Re: It's all a blur

#12

Post by srean »

sebzim4500 wrote: post #11
oulipo2 wrote: post #9

Earlier quoted context omitted.

The parade is easy: just add a small amount of random noise (even not visible to the human eye) to the blurred picture, and suddenly the "blur inversion" fails spectacularly

Does this actually work? I would have thought that, given the deconvolution step is just a linear operator with reasonable coefficients, adding a small amount of noise to the blurred image would just add similarly small amount of noise to the unblurred result.

To reconstruct the image one has to cut off those frequencies in the corrupted image where the signal to noise is poor. In many original images, the signal in high frequencies are sacrificable, so get rid of those and then invert.

https://en.wikipedia.org/wiki/Wiener_deconvolution

If one blindly inverts the linear blur transform then yes, the reconstruction would usually be a complete unrecognisable mess because the inverse operator is going to dramatically boost the noise as well.

Re: It's all a blur

#13

Post by yetihehe »

jonathanlydall wrote: post #6
praptak wrote: post #3

My (admittedly superficial) knowledge about blur reversibility is that an attacker may know what kind of stuff is behind the blur. I mean knowledge like "a human face, but the potential set of humans is known to the attacker" or even worse "a text, but the font is obvious from the unblurred part of the doc".

This was also my understanding. It's essentially like "cracking" a password when you have its hash and know the hashing algorithm. You don't have to know how to reverse the blur, you just need to know how to do it the normal way, you can then essentially brute force through all possible characters one at a time to see if it looks the same after applying the blur. Thinking about this, adding randomness to the blurring…

> just mask the sensitive data with a single color which is impossible to reverse (for rasterized images, this is not a good idea for PDFs

Also not a good idea for masking already compressed images of text, like jpg, because some of the information might bleed out in uncovered areas.

Re: It's all a blur

#14

Post by alphazard »

dsego wrote: post #7

Can this be applied to camera shutter/motion blur, at low speeds the slight shake of the camera produces this type of blur. This is usually resolved with IBIS to stabilize the sensor.

The ability to reverse is very dependent on the transformation being well known, in this case it is deterministic and known with certainty. Any algorithm to reverse motion blur will depend on the translation and rotation of the camera in physical space, and the best the algorithm could do will be limited by the uncertainty in estimating those values.

If you apply a fake motion blur like in photoshop or after effects then that could probably be reversed pretty well.

Re: It's all a blur

#16

Post by swiftcoder »

One salient point not touched on here, is that an awful lot of the time, the things folks are blurring out specifically is text. And since we know an awful lot about what text ought to look like, we have a lot more information to guide the reconstruction...

Re: It's all a blur

#17

Post by swiftcoder »

jonathanlydall wrote: post #6
praptak wrote: post #3

My (admittedly superficial) knowledge about blur reversibility is that an attacker may know what kind of stuff is behind the blur. I mean knowledge like "a human face, but the potential set of humans is known to the attacker" or even worse "a text, but the font is obvious from the unblurred part of the doc".

This was also my understanding. It's essentially like "cracking" a password when you have its hash and know the hashing algorithm. You don't have to know how to reverse the blur, you just need to know how to do it the normal way, you can then essentially brute force through all possible characters one at a time to see if it looks the same after applying the blur. Thinking about this, adding randomness to the blurring…

> mask the sensitive data with a single color which is impossible to reverse

You note the pitfall of text remaining behind the redaction in PDFs (and other layered formats), but there are also pitfalls here around alpha channels. There have been several incidents where folks drew not-quite-opaque redaction blocks over their images.

Re: It's all a blur

#18

Post by dizzant »

alphazard wrote: post #14
dsego wrote: post #7

Can this be applied to camera shutter/motion blur, at low speeds the slight shake of the camera produces this type of blur. This is usually resolved with IBIS to stabilize the sensor.

The ability to reverse is very dependent on the transformation being well known, in this case it is deterministic and known with certainty. Any algorithm to reverse motion blur will depend on the translation and rotation of the camera in physical space, and the best the algorithm could do will be limited by the uncertainty in estimating those values. If you apply a fake motion blur like in photoshop or after effects…

I recall a paper from many years ago (early 2010s) describing methods to estimate the camera motion and remove motion blur from blurry image contents only. I think they used a quality metric on the resulting “unblurred” image as a loss function for learning the effective motion estimate. This was before deep learning took off; certainly today’s image models could do much better at assessing the quality of the unblurred image than a hand-crafted metric.

Re: It's all a blur

#19

Post by jeremyscanvic »

Blur is perhaps surprisingly one of the degradations we know best how to undo. It's been studied extensively because there's just so many applications, for microscopes, telescopes, digital cameras. The usual tricks revolve around inverting blur kernels, and making educated guesses about what the blur kernel and underlying image might look like. My advisors and I were even able to train deep neural networks using only blurry images using a really mild assumption of approximate scale-invariance at the training dataset level [1].

[1] https://ieeexplore.ieee.org/document/11370202

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