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This “guessing” is nice for the sake of artistry, but we’ve got to be careful when knowing what actually was there is important—like when photos are submitted as evidence in court cases, or when determining the identity of a person from a photo as part of an investigation.

I hope such photos are submitted as camera takes them. With our without this new feature, photoshopping a photo before presenting it to court must be illegal.

If you consider photos taken by cell phones, it's hard to really say what "as the camera takes them" means - a lot of ML-driven retouching happens "automagically" with most modern cell phones already and I'd expect more in the future.

It goes even further than that. Image sensors don't capture images. They record electricity that can be interpreted as an image.

This might seem like a quibble, but once you dive a little deeper into it, you realise that there's enormous latitude and subjectivity in the way you do that interpretation.

What's even crazier is that this didn't come with digital photography. Analogue film photography has the same problem. The silver on the film doesn't become an image until it's interpreted by someone in the darkroom.

There is no such thing as an objective photograph. It's always a subjective interpretation of an ambiguous record.

Analog photography you could at least use E-6. Processing was tightly controlled and standardized, and once processed, you had an image.

The nice thing about this was that you could hand the E-6 off to a magazine and end up with a photograph printed in the magazine that was very close to the original film. Any color shifts or changes in contrast you could see just with your eyes. You could drop the film in a scanner and visually confirm that the scan looks identical to the original. (You cannot do this with C-41.)

This was not used for forensic photography, though. The point of using E-6 was for the photographer to make artistic decisions and capture them on film, so they can get back to taking photos. My understanding is that crime scene photography was largely C-41, once it was relatively cheap.

There is a difference in the degree of subjectivity. In interpreting electricity, it's highly localized, and probably doesn't affect the macro structure of the image.

With ML-enhanced photos, you might have a distanced face that is "enhanced" by the model, to become a face that wasn't there. Or a fingerprint, a birthmark, a mole, etc.

I'm surprised nobody cited yet the Gigapixel AI-"Ryan Gosling" case from last year: https://petapixel.com/2020/08/17/gigapixel-ai-accidentally-a...

In some use cases, like OCR, the accuracy of these guesses can be established in a scientific way. And it tends to be very good.

I agree; I'd say two things in response, though:

1. However good the guess is, it's still just that: a guess. Taking the standard of "evidence in a murder case", the OCR can and probably should be used to point investigators in the right direction so they can go and collect more data, but it should not be considered sufficient as evidence itself.

2. OCR is a relatively constrained solution space - success in those conditions doesn't mean the same level of accuracy can or will be reached outside of that constrained space.

To be clear, though - I'm making a primarily epistemic argument, not one based on utility. There are a lot of areas for which these kind of machine guessing systems are of enormous utility, we just shouldn't confuse what they're doing with actual data collection.

Unless they’re not: https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...

Did you read that article? That wasn't an OCR issue it was an image compression issue.

I did, and I’m aware it wasn’t OCR that was the underlying problem.

But the issue manifests as characters being incorrectly identified because of an algo t

Same thing in a way. OCR does lossy compression from pixels to text. Both could do similar mistake for pretty similar reasons.

I'm not sure about the OCR example, but there are information / sampling theory limits on what can be discerned in an image, based on sampling rate (pixels basically) and optics. Any extrapolation outside these limits is proveably guessing.

Edit - re OCR do you mean e.g. from a picture of a blurred license plate we could rule in or out a subset of possible numbers, depending on how blurred, like a B could be a 8 but not a L? (And sorry if your example is unrelated). This is valid, and unrelated to super resolution, you can do this analysis with Nyquist and point spread functions.

I think this point is worth pushing on a bit harder, which is to say that the "additional details" in the picture are guesses by the software, not actual additional details.

I don't. Everyone knows this already and it seems like a lot of people are just saying it over and over to look clever.