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Is Google's AI-Driven Image-Resizing Algorithm Dishonest? (thestack.com)
The Stack reports on Google's "new research into upscaling low-resolution images using machine learning to 'fill in' the missing details," arguing this is "a questionable stance...continuing to propagate the idea that images contain some kind of abstract 'DNA', and that there might be some reliable photographic equivalent of polymerase chain reaction which could find deeper truth in low-res images than either the money spent on the equipment or the age of the equipment will allow."
An anonymous reader summarizes their report:
Rapid and Accurate Image Super Resolution (RAISR) uses low and high resolution versions of photos in a standard image set to establish templated paths for upward scaling... This effectively uses historical logic, instead of pixel interpolation, to infer what the image would look like if it had been taken at a higher resolution.
It's notable that neither their initial paper nor the supplementary examples feature human faces. It could be argued that using AI-driven techniques to reconstruct images raises some questions about whether upscaled, machine-driven digital enhancements are a legal risk, compared to the far greater expense of upgrading low-res CCTV networks with the necessary resolution, bandwidth and storage to obtain good quality video evidence.
The article points out that "faith in the fidelity of these 'enhanced' images routinely convicts defendants."
It's notable that neither their initial paper nor the supplementary examples feature human faces. It could be argued that using AI-driven techniques to reconstruct images raises some questions about whether upscaled, machine-driven digital enhancements are a legal risk, compared to the far greater expense of upgrading low-res CCTV networks with the necessary resolution, bandwidth and storage to obtain good quality video evidence.
The article points out that "faith in the fidelity of these 'enhanced' images routinely convicts defendants."
People are using this sort of thing in court?
I think these is a very interesting field for consumer needs, but I have to agree, that's disturbing if they're allowing what... let's face it... is data made up by an AI that "looks right", to convict people.
Wingus, Dingus! Listen up!
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...from the fact that Google is run by shape-shifting reptoids.
WAKE UP, SHEEPLE!!!!! /Cue obligatory XKCD
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You can't really upscale resolution but you can "enhance" images (especially raw ones) to a point. A lot of shots may be over or underexposed with some details left in one or more of the channels but visually blocked out, having thousands of minuscule changes and filtering go through a human in the hope of seeing something would be nearly impossible and having a filter to weed them out is helpful.
JPEG and similar compression are like MP3 - you can filter out what the algorithm defines as outside of the human realm to perceive but a lot of those assumptions are faulty leading to noticeable artifacts. However it is very hard to recover the data lost in "lossy compression" although you can make some assumptions to recover them.
The other problem with using these filters is that they're called artificial intelligences. They are not intelligent and calling them that leads to an assumption of infallibility. They're a form of Bayesian filtering and we've been using that since at least the days of OS/2 to "enhance" images, I used a demo of a program back then that did just that: inferences on JPEG to make a type of vector image. We just use more powerful clock cycles and more storage to have them perform better but they're not and never will be magic.
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> They are not intelligent and calling them that leads to an assumption of infallibility.
That's an interesting comment. I'd think the opposite. I'm intelligent, and often wrong. Gears are dumb, and always perform multiplication correctly, never giving the wrong result. To me, intelligence implies the ability to come up with different answers, some of which may be wrong. If it can't come up with unexpected answers, it's just a dumb machine, I'd think.
Enhancing an image for increased resolution isn't dishonest... unless you present it as the absolute truth. The reality is that it's a probabilistic view of the unenhanced version which is to say that it probably looks as presented in the image but there are other possibilities that could match that image. Honestly, I doubt it's worse than a human's memory of image because human's don't store information as PNGs and our recall is far from perfect.
Anons need not reply. Questions end with a question mark.
All upscaling algorithms are making up data based on assumptions on what "typical" hi-res images should look like given their low-res counterparts. That doesn't mean they are lying or misrepresenting. Furthermore, some assumptions are most statistically valid than others, and some produce more aesthetically pleasing results than others, actually resulting in images that are genuinely more likely to be closer to the true image than nearest neighbor.
Nowhere in google's paper are they suggesting that these images be used for forensic purposes, nor claiming that they are finding "deeper truth" or additional information in the images than what actually exists. They developed an approach that produces better results for common classes of images than previous algorithms, which is useful for a large number of applications that don't require the same level of rigor that forensics do.
You can't get something from nothing. That's a fact. Humans can fill in some gaps and AI could probably do the same, but there is no guarantee the results are correct.
On the other hand, if it could actually discern more from a video (which humans can also do, but probably not quite as well), it might be able to "enhance" individual images to some extent and have accurate results.
That people can be convicted by the results is a little scary, but at some level no different from a jury misinterpreting a low resolution image. Aside from the fact it was a single opinion that swayed that of the entire jury.
This.
I've been in the business 49 years starting when the slide rule was the calculator of choice.
"Artificial Intelligence" (AI) started with a basic definition that always circled back to the human brain as a reference for "intelligence."
In later years, a more realistic description of AI required us to drop the human brain part, but many people failed to catch the move.
A machine will only be intelligent when it can commit suicide because Facebook is down.
It little behooves the best of us to comment on the rest of us.
I had a friend who often pointed out that a common definition of "life" (from the first Google hit for "definition of life": growth, reproduction, functional activity, and continual change preceding death) only works if you exclude fire.
"National Security is the chief cause of national insecurity." - Celine's First Law
Perhaps infallible is the wrong word.
The problem to the lay person is that the 'AI' in contemporary media is portrayed as a sort of super-intelligence that is purely logical and thus superior to humans (and subsequently morally 'better' as well). It's easy to say by an attorney that a non-human, self-aware entity enhanced a perfect digital replica of the scene, it is therefore free of any human bias and thus a 'perfect' proof.
To go with your gears example, when people use gears all the time and they're always right, imagine you develop a complex gear system that can do something no human has ever done nor can feasibly verify and you call it an 'mechanical intelligence', people will assume it's always right given the prior simple gears have always been right.
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I had thought of the possibility of this years ago. The basic idea is that, if you downsample an image, and then upsample it again, information is lost in the low resolution version that must be reconstructed somehow. Essentially what you need is a means to make educated guesses as to the missing information. Traditional codecs are based on the maths that results when the codec is intended to reconstruct an arbitrary image. If we constrain the space of possible images, such as photos of the same person, the amount of information necessary to specify the image is less. Where deep learning comes in is finding convenient subsets of 'all possible images' such that, if we assume an image lies within a given subset of 'all possible images', we can make better guesses as to the missing information than if the image was totally arbitrary.
John_Chalisque
I thought this was just something TV / moviemakers had been doing since the 90s to purposefully annoy geeks.
"Zoom in on D2."
"Enhance!"
What... what did I just read?
I wonder if this was a botpost or a human typing.
"Oh shit, surgery marks, they are FAKE, there goes my woody."
Table-ized A.I.
This should be easy for a defense attorney to invalidate. Hallucinated images (assembled largely from a corpus of previous images to "enhance" some evidence) are not the same as an image that is run through an abstract de-blurring algorithm.
It's probably easy to demonstrate the problem with some examples, so that judge and jury "gets it".
These comments are mine; I do not speak for my employer.
You know, in a bidding police-state it is far more important to get convictions than to convict the person that actually did it. "Tools" like this (and as an engineer and scientist, I am offended by the very idea that has been implemented here) are a welcome way to make it appear that everything is in order.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
The AI is not dishonest, it has been designed to make-up stuff.
Its a bit like doing the fractal compression of an image, then restore to an higher resolution than the original. You will get a more detailed image, but it's content will have been made-up.
Fractal compression existed well before Google and no idiot used this feature as proof AFAIK.
I cannot believe anybody in his right mind would take any "make-up" algorithm as reliable evidence. One has to be pretty ignorant, or criminally insane, to use what is a (very nice) party-trick in a court of laws.
Irrelevant news and morons using moderation to mod down what they disagree on. 2018 resolution: so long.
As NCIS episodes have demonstrated, the video analysts have to issue the command Enhance! for this thing not to lie
WARNING: Smartphones have side effects--most of them undocumented.
How did your DNA get in the house? Really?
1) False match.
2) Carried in by animals, insects, etc.
3) On the sole of someone's shoe.
4) From dumpster-diving.
5) Planted, by cops or others.
I could go on all day.
Holy shit!
Remember when they found that bank loan "artificial intelligence" programs were discriminating based on the racial profile of your zip code? The program learned from the human examples they were given.
So it isn't impossible that algorithms that insert "likely" pixels into images would perhaps add minority colored pixels in an urban looking scene and white colored pixels in a suburban scene. You can't use image data that didn't come from the actual scene in court!!!!
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