There was a scandal when it was discovered that Xerox machines were doing this; in that case, the example showed "photocopies" replacing numbers in documents with other numbers.
During my PhD this issue came up amongst those in the group looking into compressed sensing in MRI. Many reconstruction methods (AI being a modern variant) work well because a best guess is visually plausible. These kinds of methods fall apart when visually plausible and "true" are different in a meaningful way. The simplest examples here being the numbers in scanned documents, or in the MRI case, areas of the brain where "normal brain tissue" was on average more plausible than "tumor".
[1]: http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...
Compression that gives you a blurred image is a trade-off.
But what does it mean to “be aware of” compression that may give you a crisp image of some made up document?
This isn't an obvious statement to me. If you've had the misfortune of scanning documents to PDF and getting the 100MB per page files automatically emailed to you then you might see the benefit in all that white space being compressed somehow.
> But what does it mean to “be aware of” compression that may give you a crisp image of some made up document?
This isn't something I said. A good compression system for documents will not change characters in any circumstances.
idk if I had to second guess every single result coming out of a machine it would be a showstopper for me. This isn't pokemon go, tumor detection is serious matter
But say you're doing noisy measurements, and you are under-measuring like you say, and you have to fabricate non-random non-homogenous reconstruction noise. In that case it would be a very good idea to produce, as they do for lossy compression, both the standard overall bit rate vs. PSNR characterization against alternate direct (non-sparse) measurement ground truths (that have to exist, or else the reconstruction method should be called into question), and the bit rate for each particular sparse measurement. So this way people can see how reliable the reconstruction is. Ideally the image should be labeled at the pixel level with reconstruction probabilities, or presented in other ways to demonstrate the ratio of measured vs. fabricated information, like 95% confidence-interval extremal reconstructions or something.
It's not clear that community is doing this level of due diligence, so then the voices here are right: it's not a good idea to use.
It's perfectly possible to build neural network based compression systems that do not output false information.