Submitted 1 month ago by Stopthatgirl7@lemmy.world to technology@lemmy.world
https://gizmodo.com/ai-image-chatgpt-court-ban-video-evidence-enhanced-llm-1851382045
A judge in Washington state has blocked video evidence that’s been “AI-enhanced” from being submitted in a triple murder trial. And that’s a good thing, given the fact that too many people seem to think applying an AI filter can give them access to secret visual data.
If you ever encountered an AI hallucinating stuff that just does not exist at all you know how bad the idea of AI enhanced evidence actually is.
Everyone uses the word “hallucinate” when describing visual AI because it’s normie-friendly and cool sounding, but the results are a product of math. Very complex math, yes, but computers aren’t taking drugs and randomly pooping out images because computers can’t do anything truly random.
You know what else uses math? Basically every image modification algorithm, including resizing. I wonder how this judge would feel about viewing a 720p video on a 4k courtroom TV because “hallucination” takes place in that case too.
There is a huge difference between interpolating pixels and inserting whole objects into pictures.
normie-friendly
Whenever people say things like this, I wonder why that person thinks they’re so much better than everyone else.
Has this argument ever worked on anyone who has ever touched a digital camera? “Resizing video is just like running it through AI to invent details that didn’t exist in the original image”?
“It uses math” isn’t the complaint and I’m pretty sure you know that.
It’s not AI, it’s PISS. Plagiarized information synthesis software.
computers aren’t taking drugs and randomly pooping out images
Sure, no drugs involved, but they are running a statistically proven random number generator and using that (along with non-random data) to generate the image. The result is this - ask for the same image, get two different images:
computers can’t do anything truly random.
Technically incorrect - computers can be supplied with sources of entropy, so while it’s true that they will produce the same output given identical inputs, it is in practice quite possible to ensure that they do not receive identical inputs if you don’t want them to.
No computer algorithm can accurately reconstruct data that was never there in the first place.
Ever.
This is an ironclad law, just like the speed of light and the acceleration of gravity. No new technology, no clever tricks, no buzzwords, no software will ever be able to do this.
Ever.
If the data was not there, anything created to fill it in is by its very nature not actually reality. This includes digital zoom, pixel interpolation, movement interpolation, and AI upscaling. It preemptively also includes any other future technology that aims to try the same thing, regardless of what it’s called.
One little correction, digital zoom is not something that belongs on that list. It’s essentially just cropping the image. That said, “enhanced” digital zoom I agree should be on that list.
Digital zoom is just cropping and enlarging. You’re not actually changing any of the data. There may be enhancement applied to the enlarged image afterwards but that’s a separate process.
But the fact remains that digital zoom cannot create details that were invisible in the first place due to the distance from the camera to the subject. Modern implementations of digital zoom always use some manner of interpolation algorithm, even if it’s just a simple linear blur from one pixel to the next.
The problem is not in how a digital zoom works, it’s on how people think it works but doesn’t. A lot of people (i.e. [l]users, ordinary non-technical people) still labor under the impression that digital zoom somehow makes the picture “closer” to the subject and can enlarge or reveal details that were not detectable in the original photo, which is a notion we need to excise from people’s heads.
Hold up. Digital zoom is, in all the cases I’m currently aware of, just cropping the available data. That’s not reconstruction.
Otherwise, yep, I’m with you there.
See this follow up:
Digital zoom makes the image bigger but without adding any detail (because it can’t). People somehow still think this will allow you to see small details that were not visible before.
There’s a specific type of digital zoom which captures multiple frames and takes advantage of motion between frames to interpolate to get higher detail. This is rather limited because you need a lot of sharp successive frames just to get a solid 2-3x resolution with minimal extra noise.
It preemptively also includes any other future technology that aims to try the same thing
No it doesn’t. For example you can, with compute power, for distortions introduced by camera lenses/sensors/etc and drastically increase image quality. For example this photo of pluto was taken from 7,800 miles away:
The unprocessed image would look nothing at all like that. There’s a lot more data in an image than you can see with the naked eye, and algorithms can extract/highlight the data. That’s obviously not what a generative ai algorithm does, those should never be used, but there are other algorithms which are apropriate.
No computer algorithm can accurately reconstruct data that was never there in the first place.
What you are showing is (presumably) a modified visualisation of existing data. That is: given a photo which known lighting and lens distortion, we can use math to display the data (lighting, lens distortion, and input registered by the camera) in a plethora of different ways. You can invert all the colours if you like. It’s still the same underlying data. Modifying how strongly certain hues are shown, or correcting for known distortion are just techniques to visualise the data in a clearer way.
“Generative AI” is essentially just non-predictive extrapolation based on some data set, which is a completely different ball game, as you’re essentially making a blind guess at what could be there, based on an existing data set.
None of your examples are creating new legitimate data from the whole cloth. They’re just making details that were already there visible to the naked eye. We’re not talking about taking a giant image that’s got too many pixels to fit on your display device in one go, and just focusing on a specific portion of it. That’s not the same thing as attempting to interpolate missing image data. In that case the data was there to begin with, it just wasn’t visible due to limitations of the display or the viewer’s retinas.
The original grid of pixels is all of the meaningful data that will ever be extracted from any image (or video, for that matter).
Your wedding photographer’s picture actually throws away color data in the interest of contrast and to make it more appealing to the viewer. When you fiddle with the color channels like that and see all those troughs in the histogram that make it look like a comb? Yeah, all those gaps and spikes are actually original color/contrast data that is being lost. There is less data in the touched up image than the original, technically, and if you are perverse and own a high bit depth display device (I do! I am typing this on a machine with a true 32-bit-per-pixel professional graphics workstation monitor.) you actually can state at it and see the entirety of the detail captured in the raw image before the touchups. A viewer might not think it looks great, but how it looks is irrelevant from the standpoint of data capture.
This is just smarter post processing, like better noise cancelation, error correction, interpolation, etc.
But ML tools extrapolate rather than interpolate which adds things that weren’t there
offtopic: I like the picture on the left more. It feels more alive. Colder in color, but warmer in expression. Dunno how to say that. And I’ve been in a forest yesterday, so my perception is skewed.
If people don’t get the second law of thermodynamics, explaining this to them is useless.
In my first year of university, we had a fun project to make us get used to physics. One of the projects required filming someone throwing a ball upwards, and then using the footage to get the maximum height the ball reached, and doing some simple calculations to get the initial velocity of the ball (if I recall correctly).
One of the groups that chose that project was having a discussion on a problem they were facing: the ball was clearly moving upwards on one frame, but on the very next frame it was already moving downwards. You couldn’t get the exact apex from any specific frame.
So one of the guys, bless his heart, gave a suggestion: “what if we played the (already filmed) video in slow motion… And then we filmed the video… And we put that one in slow motion as well? Maybe do that a couple of times?”
A friend of mine was in that group and he still makes fun of that moment, to this day, over 10 years later. We were studying applied physics.
That’s wrong. With a degree of certainty, you will always be able to say that this data was likely there. And because existence is all about probabilities, you can expect specific interpolations to be an accurate reconstruction of the data. We do it all the time with resolution upscaling, for example. But of course, from a certain lack of information onward, the predictions become less and less reliable.
No computer algorithm can accurately reconstruct data that was never there in the first place.
Okay, but what if we’ve got a computer program that can just kinda insert red eyes, joints, and plums of chum smoke on all our suspects?
Well that’s a bit close minded.
Perhaps at some point we will conquer quantum mechanics enough to be able to observe particles at every place and time they have ever and will ever exist. Do that with enough particles and you’ve got a de facto time machine, albeit a read-only one.
So many things we believe to be true today suggest this is not going to happen. The uncertainty principle, and the random nature of nuclear decay chief among them. The former prevents you gaining the kind of information you would need to do this, and the latter means that even if you could, it would not provide the kind of omniscience one might assume.
Complexity relates nonlinearly to the amount of moving parts.
We might be able to spend an ungodly amount of energy to do that for one particle for an hour of its existence.
Being able to build a computer (in a wide sense) that can emulate in short time (less than human life) processes consistent of more energy than was spent on its creation - it’s something else.
By your argument, nothing is ever real, so let’s all jump on a chasm.
There’s a grain of truth to that. Everything you see is filtered by the limitations of your eyes and the post-processing applied by your brain which you can’t turn off. That’s why you don’t see the blind spot on your retinas where your optic nerve joins your eyeball, for instance.
You can argue what objective reality is from within the limitations of human observation in the philosophy department, which is down the hall and to your left. That’s not what we’re talking about, here.
From a computer science standpoint you can absolutely mathematically prove the amount of data that is captured in an image and, like I said, no matter how hard you try you cannot add any more data to it that can be actually guaranteed or proven to reflect reality by blowing it up, interpolating it, or attempting to fill in patterns you (or your computer) think are there. That’s because you cannot prove, no matter how the question or its alleged solution are rephrased, that any details your algorithm adds are actually there in the real world except by taking a higher resolution/closer/better/wider spectrum image of the subject in question to compare. And at that point it’s rendered moot anyway, because you just took a higher res/closer/better/wider/etc. picture that contains the required detail, and the original (and its interpolation) are unnecessary.
Entropy and information theory is very real, it’s embedded in quantum physics
I think we need to STOP calling it "Artificial Intelligence". IMHO that is a VERY misleading name. I do not consider guided pattern recognition to be intelligence.
A term created in order to vacuum up VC funding for spurious use cases.
It’s the new “4k”. Just buzzwords to get clicks.
My disappointment when I realised “4k” was only 2160p 😔
on the contrary! it’s a very old buzzword!
AI should be called machine learning. much better. If i had my way from here on it would be called “fancy curve fitting” from here on.
Optical Character Recognition used to be firmly in the realm of AI until it became so common that even the post office users it. Nowadays, OCR is so common that instead of being proper AI, it’s just another mundane application of a neural network. I guess, eventually Large Language Models will be outside there scope of AI.
You, and humans in general, are also just sophisticated pattern recognition and matching machines. If neural networks are not intelligent, then you are not intelligent.
This may be the dumbest statement I have yet seen on this platform. That’s like equating a virus with a human by saying both things replicate themselves so they must be similar.
You can say what you like but absolutely zero true and full understand of what human intelligence actually is or how it works.
“AI”, or whatever you want to call it, is not at all similar.
I do not consider guided pattern recognition to be intelligence.
That’s a you problem, this debate happened 50 years ago and we decided Intelligence is the right word.
You forget that we can change these definitions any time we see fit.
What is the definition of intelligence? Does it require sentience? Can a data set be intelligently compiled into interesting results without human interaction? Yes the term AI is stretched a bit thin but I believe it has enough substance to qualify.
How is guided pattern recognition is different from imagination (and therefore intelligence) though?
There’s a lot of other layers in brains that’s missing in machine learning. These models don’t form world models and some have an understanding of facts and have no means of ensuring consistency, to start with.
Your comment is a good reason why these tools have no place in the courtroom: The things you describe as imagination.
They’re image generation tools that will generate a new, unrelated image that happens to look similar to the source image. They don’t reconstruct anything and they have no understanding of what the image contains. All they know is which color the pixels in the output might probably have given the pixels in the input.
It’s no different from giving a description of a scene to an author, asking them to come up with any event that might have happened in such a location and then trying to use the resulting short story to convict someone.
I agree. It’s restricted intelligence (RI), at best, and even that can be argued against.
My Concious Cognative Correlator is the real shit.
How long until we got upscalers of various sorts built into tech that shouldn’t have it? For bandwidth reduction, for storage compression, or cost savings. Can we trust what we capture with a digital camera, when companies replace a low quality image of the moon with a professionally taken picture, at capture time? Can sport replays be trusted when the ball is upscaled inside the judges’ screens? Cheap security cams with “enhanced night vision” might get somebody jailed.
I love the AI tech. But its future worries me.
AI-based video codecs are on the way. This isn’t necessarily a bad thing because it could be designed to be lossless or at least less lossy than modern codecs. But compression artifacts will likely be harder to identify as such. That’s a good thing for film and TV, but a bad thing for, say, security cameras.
The devil’s in the details and “AI” is way too broad a term. There are a lot of ways this could be implemented.
I don’t think loss is what people are worried about, really - more injecting details that fit the training data but don’t exist in the source.
Given the hoopla Hollywood and directors made about frame-interpolation, do you think generated frames will be any better/more popular?
I don't think AI codecs will be anything revolutionary. There are plenty of lossless codecs already, but if you want more detail, you'll need a better physical sensor, and I doubt there's anything that can be done to go around that (that actually represents what exists, not an hallucination).
AI-based video codecs are on the way.
Arguably already here.
Look at this description of Samsungs mobile AI for their S24 phone and newer tablets:
AI-powered image and video editing
Galaxy AI also features various image and video editing features. If you have an image that is not level (horizontally or vertically) with respect to the object, scene, or subject, you can correct its angle without losing other parts of the image. The blank parts of that angle-corrected image are filled with Generative AI-powered content. The image editor tries to fill in the blank parts of the image with AI-generated content that suits the best. You can also erase objects or subjects in an image. Another feature lets you select an object/subject in an image and change its position, angle, or size.
It can also turn normal videos into slow-motion videos. While a video is playing, you need to hold the screen for the duration of the video that you want to be converted into slow-motion, and AI will generate frames and insert them between real frames to create a slow-motion effect.
It will wild out for the foreseeable future until the masses stop falling for it in gimmicks then it will be reserved for the actual use cases where it’s beneficial once the bullshit ai stops making money.
Lol, you think the masses will stop falling for it in gimmicks? Just look at the state of the world.
Not all of those are the same thing. AI upscaling for compression in online video may not be any worse than "dumb" compression in terms of loss of data or detail, but you don't want to treat a simple upscale of an image as a photographic image for evidence in a trial. Sport replays and hawkeye technology doesn't really rely on upscaling, we have ways to track things in an enclosed volume very accurately now that are demonstrably more precise than a human ref looking at them. Whether that's better or worse for the game's pace and excitement is a different question.
The thing is, ML tech isn't a single thing. The tech itself can be used very rigorously. Pretty much every scientific image you get from a telescope is compiled using ML or the data is processed using ML. That's not a problem if done correctly. The issue is everybody is both assuming "generative AI" chatbots, upscalers and image processers are what ML is and people keep trying to apply those things directly in the dumbest possibel way thinking it is basically magic.
I'm not particularly afraid of "AI tech", but I sure am increasingly annoyed at the stupidity and greed of some of the people peddling it, criticising it and using it.
Cheap security cams with “enhanced night vision” might get somebody jailed.
Might? We’ve been arresting the wrong people based on shitty facial recognition for at least 5 years now. This article has examples from 2019.
On one hand, the potential of this type of technology is impressive. OTOH, the failures are super disturbing.
Probably not far. NVidia has had machine learning enhanced upscaling of video games for years at this point, and now they’ve also implemented similar tech but for frame interpolation. The rendered output might be 720p at 20FPS but will be presented at 1080p 60FPS.
It’s not a stretch to assume you could apply similar tech elsewhere. Non-ML enhanced, yet still decently sophisticated frame interpolation and upscaling has been around for ages.
I’d love to see the “training data” for this model, but I can already predict it will be 99.999% footage of minorities labelled ‘criminal’.
And cops going “Aha! Even AI thinks minorities are committing all the crime”!
Tell me you didn’t read the article without telling me you didn’t read the article
Jesus Christ, does this even need to be pointed out!??
Imagine a prosecution or law enforcement bureau that has trained an AI from scratch on specific stimuli to enhance and clarify grainy images. Even if they all were totally on the up-and-up (they aren’t, ACAB), training a generative AI or similar on pictures of guns, drugs, masks, etc for years will lead to internal bias. And since AI makers pretend you can’t decipher the logic (I’ve literally seen compositional/generative AI that shows its work), they’ll never realize what it’s actually doing.
So then you get innocent CCTV footage this AI “clarifies” and pattern-matches every dark blurb into a gun. Black iPhone? Maybe a pistol. Black umbrella folded up at a weird angle? Clearly a rifle. And so on. I’m sure everyone else can think of far more frightening ideas like auto-completing a face based on previously searched ones or just plain-old institutional racism bias.
According to the evidence, the defendant clearly committed the crime with all 17 of his fingers. His lack of remorse is obvious by the fact that he’s clearly smiling wider than his own face.
AI enhanced = made up.
clickity clackity
“ENHANCE”
The fact that it made it that far is really scary.
I’m starting to think that yes, we are going to have some new middle ages before going on with all that “per aspera ad astra” space colonization stuff.
Why not make it a fully AI court and save time if they were going to go that way. It would save so much time and money.
Of course it wouldn’t be very just, but then regular courts aren’t either.
This actually opens an interesting debate.
Every photo you take with your phone is post processed. Saturation can he boosted, light levels adjusted, noise removed, night mode, all without you being privy as to what’s happening.
Typically people are okay with it because it makes for a better photo - but is it a true representation of the reality it tried to capture? Where is the line of the definition of an ai-enhanced photo/video?
We can currently make the judgement call that a phones camera is still a fair representation of the truth, but what about when the 4k AI-Powered Night Sight Camera does the same?
My post is more tangentially related to original article, but I’m still curious as what the common consensus is.
You’d think it would be obvious you can’t submit doctored evidence and expect it to be upheld in court.
For example, there was a widespread conspiracy theory that Chris Rock was wearing some kind of face pad when he was slapped by Will Smith at the Academy Awards in 2022. The theory started because people started running screenshots of the slap through image upscalers, believing they could get a better look at what was happening.
Sometimes I think, our ancestors shouldn’t have made it past the ocean.
Just for now, soon this practice will be normalized and widely used, after all we are in the late capitalism stage and all violations are relativized
“Just so we’re clear guys, fake evidence is not allowed”
We do not need AI pulling a George Lucas.
How does this work when you have a shitty Samsung that turns a pic of a pic of the moon into the Moon by adding details that weren’t there?
Used to be that people called it the “CSI Effect” and blamed it on television.
Funny thing. While people worry about unjust convictions, the “AI-enhanced” video was actually offered as evidence by the defense.
During Kyle Rittenhouse’s trial the defense attorney objected to using the pinch to zoom feature of an iPad because it (supposedly) used AI. This was upheld by the judge so the prosecution couldn’t zoom in on the video.
“When we enhance the image and place a knife in the defendant’s hand…”
Sure, no algorithm is able to extract any more information from a single photo. But how about combining detail caught in multiple frames of video? Some phones already do this kind of thing, getting multiple samples for highly zoomed photos thanks to camera shake.
Still, the problem remains that the results from a cherry-picked algorithm or outright hand-crafted pics maybe presented.
Yeah, this is a really good call. I’m a fan of what we can do with AI, when you start looking at those upskilled videos with a magnifying glass… It’s just making s*** up that looks good.
This is the best summary I could come up with:
A judge in Washington state has blocked video evidence that’s been “AI-enhanced” from being submitted in a triple murder trial.
And that’s a good thing, given the fact that too many people seem to think applying an AI filter can give them access to secret visual data.
Lawyers for Puloka wanted to introduce cellphone video captured by a bystander that’s been AI-enhanced, though it’s not clear what they believe could be gleaned from the altered footage.
For example, there was a widespread conspiracy theory that Chris Rock was wearing some kind of face pad when he was slapped by Will Smith at the Academy Awards in 2022.
Using the slider below, you can see the pixelated image that went viral before people started feeding it through AI programs and “discovered” things that simply weren’t there in the original broadcast.
Large language models like ChatGPT have convinced otherwise intelligent people that these chatbots are capable of complex reasoning when that’s simply not what’s happening under the hood.
The original article contains 730 words, the summary contains 166 words. Saved 77%. I’m a bot and I’m open source!
Nor evidence, for that matter
Stovetop@lemmy.world 1 month ago
“Your honor, the evidence shows quite clearly that the defendent was holding a weapon with his third arm.”
Cheems@lemmy.world 1 month ago
Image
Isoprenoid@programming.dev 1 month ago
RIP Charlie Robinson