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buildbot · 3 years ago
Interestingly, reading the fourth chapter, the first time this technique of identifying the sensor based on the specific noise characteristics was 2005. I bet this was tacitly well known far before 2005.

There are many camera from pre - 2005 that did dark frames and flat frames, for example Sinar digital backs. They all came with a CD with the unique calibration file for the back to tune out these characteristics to a large degree. Without them, you can see literally see the difference in exposure down the center of the image from the two stitched lithography mask shots required to make large CCDs at the time.

formerly_proven · 3 years ago
Most full-frame sensors are still stitched from two exposures side-by-side (digital MF is four exposures, real medium format sensors six), but they're matched well-enough (or the raw files are already compensating for this) that it's only an issue for the astro people nowadays.
shrx · 3 years ago
Interesting, I'm an amateur astronomer and didn't know that, I've never noticed any stitching artifacts when shooting with my Canon 6D. Found a relevant discussion on DPreview[1] which suggests that not all cameras do this, it appears to be more noticeable on some Sony cameras at first glance of the thread.

[1] https://www.dpreview.com/forums/thread/3949870

edit: another discussion: https://www.dpreview.com/forums/thread/3949529

buildbot · 3 years ago
Based on my experience with Sinar digital backs, which spit out a raw, bias, dark and flat frame all independently, I think most cameras include this calibration hardcoded into the raw, because otherwise it is extremely easy to see! I know Phase One backs have calibrations that compensate for this, column errors, and hot pixels in their IIQ raws by default.
muhehe · 3 years ago
Does this survive jpeg (or similar) compression or do you need raw file for this?
mistrial9 · 3 years ago
any "lossy" compression changes the content data
neodypsis · 3 years ago
A problem I see is that someone could denoise an image and then add adversarial noise so that it resembles one of these signatures.
cookieperson · 3 years ago
Removing noise is difficult if not impossible. The best someone could probably do is find a sensor whose noise characteristics are a superposition of their cameras and another additive distribution. There are simple ways to defeat this, but it's best not to share them, as I think the only people ruñning from this kind of tracking would be sketchy criminals.
JohnFen · 3 years ago
> Removing noise is difficult if not impossible.

Completely removing noise is difficult-to-impossible. Attenuating the noise enough to impart a new noise pattern is much easier, though (not saying it's easy, just easier).

I did this sort of thing decades ago as part of larger system used for research.

ersinesen · 3 years ago
Removing is hard but not impossible: https://www.esenbil.com/photoclean
tokai · 3 years ago
Would taping a minor radiation source to the camera body be enough to scramble the noise fingerprint?
keskival · 3 years ago
If you can recognize such a fingerprint, you can also trivially fake it or wipe it with an optimizer in the loop with the recognizer.

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