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"The Edge AIsle" brings you to the forefront of artificial intelligence and edge computing, powered by Hailo.ai. In this podcast, we explore how edge AI is reshaping industries from smart cities and intelligent video analytics to autonomous vehicles and retail innovation. Join industry experts and thought leaders as they share insights into how Hailo’s AI processors are transforming the way industries function, enabling real-time deep learning and AI-driven applications directly on edge devices.
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If you're passionate about the nuts and bolts of AI processors and how they integrate with edge platforms to deliver unparalleled performance, "The Edge AIsle" is the podcast for you. Expect detailed analysis and a peek behind the curtain at the future of edge computing AI.
00:00:00:01 - 00:00:12:11
Welcome to another Hailo AI podcast, bringing you innovations and insights into AI on the edge. Now let's get started.
00:00:12:11 - 00:00:32:16
All right, so you ready to jump into something pretty crucial today? We're talking about AI and video, how AI is changing video enhancement and what that means for like actually being able to trust video evidence when we need it most. You know, in those investigations, in court, those situations. Yeah. Big stuff, especially as these tools get more powerful.
00:00:32:16 - 00:00:53:06
Right? Absolutely. You sent over this article from Hailo, and it really gets into the heart of it. Specifically, it focuses on what AI video enhancement means for something called forensic validity. I think our mission today is to really understand, like, really get down to the nuts and bolts of how these AI tools impact the reliability of video.
00:00:53:09 - 00:01:13:05
Makes sense. We see AI getting used everywhere now, so it's critical to know if we can still rely on video evidence once an AI has, you know, done its thing on it. Exactly. I mean, that's the big question here, isn't it? Once an AI has touched a video, can we still trust it? Or are we kind of entering this world where, like all visual evidence becomes a bit, well, questionable?
00:01:13:05 - 00:01:35:03
That's really it, isn't it? And it's a question that gets more and more complex as AI technology just keeps advancing. So true. So maybe we should start with the basics here. Like what actually makes video evidence hold up in court? What does it mean for video to be forensically sound in the first place? Right. And the Hailo article lays out some key elements for this.
00:01:35:03 - 00:01:55:16
Like first, you need to be sure that the video is a faithful representation of the original scene. You know, it hasn't been manipulated to show something that didn't happen. Make sense? No funny business. Exactly. Then there's the whole idea of a clear chain of custody. We need to know exactly who handled the video. When and for what purpose.
00:01:55:16 - 00:02:18:00
From the moment it was captured to the moment it's presented as evidence. So, like a complete history of the video's journey with no gaps where tampering could have happened like a paper trail, but for digital evidence. Exactly. And then there's the integrity of the data attached to the video, the metadata. This includes things like the date and time of recording, the type of camera used, and even location information.
00:02:18:02 - 00:02:38:00
All of this needs to be intact and verifiable, like the video's birth certificate, almost. In a way, yeah. And finally, then maybe most importantly, there has to be a way to actually validate, technically, that the video's meaning hasn't been altered in any way, that no one has tampered with it in a way that changes its value as evidence.
00:02:38:04 - 00:02:59:02
Okay, so it's not just about what we see on the screen. It's also about the entire story behind that video and whether we can be absolutely certain that story hasn't been well rewritten in a way that could influence an investigation. Right. The Hailo article uses this word provenance, which captures that idea perfectly. Like who recorded it, when, where, what equipment they used.
00:02:59:04 - 00:03:29:18
It's about establishing a solid origin story for the video. If you don't have that, you're already starting off on shaky ground. It's interesting though, right? Because these concerns about protecting digital evidence, they're not new. I mean, even before AI was this sophisticated, there were already protocols for handling evidence like this. Absolutely. And the Hailo article makes sure to mention the important work done by groups like SWG, which stands for the Scientific Working Group on Digital Evidence, and also NIST, the National Institute of Standards and Technology.
00:03:29:20 - 00:03:45:16
These folks have been instrumental in setting the standards for how we handle digital evidence, but it seems like AI is now forcing us to rethink and maybe even upgrade those standards. It's almost like AI is playing a whole new game with a whole new set of rules. You got it. And it's not just the rules of the game.
00:03:45:17 - 00:04:08:17
AI is even questioning the game board itself. Like what does evidence even mean in a world where AI can manipulate reality so convincingly? Right, right. And that brings us to this thing called C2PA, which if I understand correctly, is all about establishing some kind of trust and authenticity in a world flooded with AI-generated content. And Hailo seems to be right in the thick of it.
00:04:08:19 - 00:04:33:08
What's the story there? So C2PA stands for the Coalition for Content Provenance and Authenticity. And their whole mission is to tackle these challenges to content authenticity head-on. They're developing standards that everyone can use to verify if something is real or, well, AI-generated. And Hailo's work specifically contributes to what's called device-level authenticity attestation. That's really noteworthy.
00:04:33:09 - 00:04:54:18
Device-level authenticity attestation. That's a mouthful. What does that even mean? Haha, yeah, it's a bit jargony, but think of it like this. Imagine if your camera or your phone could embed a kind of digital signature into every photo or video. It captures a signature that proves that the content hasn't been tampered with since that very first moment.
00:04:54:20 - 00:05:10:18
So it's like that digital birth certificate we were talking about earlier, built right into the file from the get-go. Exactly. And that makes it way harder for someone to mess with the content later on without leaving a trace. If you try to change something, that signature won't match up anymore. And everyone knows something's fishy. Okay, now that makes sense.
00:05:10:20 - 00:05:31:22
But then the Hailo article brings up this other really important distinction. And I think this is key to what we're talking about today. They talk about the difference between AI restoration and AI generation. And honestly, for someone who's not a tech wizard, I'm not entirely sure I get why this difference is so important, especially when it comes to, you know, evidence and all that.
00:05:31:22 - 00:05:54:03
Right. So at the most basic level, any AI that's trying to improve the quality of an image or video, what it's really trying to do is improve what's called the signal-to-noise ratio, or SNR for short. Got it. So think of signal as the actual information we want to see—the details of the scene—and noise is all the stuff that gets in the way, like graininess, blurriness, bad lighting, all that.
00:05:54:04 - 00:06:12:18
AI tries to clean up that noise to help us see the signal better. Okay, that makes sense. Signal is good. Noise is bad. Basically. But here's the thing. AI can tackle this problem in two very different ways. And this is where things get interesting and a bit tricky for forensic validity. Okay, I'm listening. Two ways to deal with noise—what are they?
00:06:12:18 - 00:06:30:02
The first one is called image restoration. And this is all about getting rid of that noise while trying to keep the original signal as intact as possible. It's like, imagine you're cleaning a dusty window. You want to get rid of the dust to see the view better. Yeah. Makes sense. So restoration is kind of like that.
00:06:30:03 - 00:06:55:11
It uses clever math to understand the noise and then carefully remove it. The goal is to reveal a cleaner version of what was already there, not to create something new. So it's about bringing out the truth that's hidden beneath the noise, not about making stuff up. Exactly. And that's crucial for forensics, because in a legal setting, you need to be sure that what you're seeing is actually what the camera captured, not what an AI decided to invent.
00:06:55:11 - 00:07:15:00
Right. I'm following. So what's the other way AI can handle this? You said there were two, right? So the second way is called image regeneration. And this is where things get, well, a bit more complicated, because now the AI isn't just tidying things up. It's trying to actually add to the signal to enhance the, what we call the semantic content of the image.
00:07:15:00 - 00:07:34:19
Semantic content sounds complicated. It basically means the AI is trying to understand what it's looking at and then fill in the gaps, even create completely new visual details based on that understanding. Like if there's a blurry face in the video, the AI might try to generate a clearer version of the face, even if it has to, you know, make up some of the features.
00:07:34:19 - 00:07:54:16
Oh, hold on. So it's not just cleaning things up, it's actually like adding details that might not have been there originally, like it's painting in things based on what it thinks should be there. Yeah, that's a good way to put it. And that's where the big problem for forensic evidence comes in. Right. Because now you're no longer looking at a true and accurate record of what happened.
00:07:54:21 - 00:08:19:05
You're looking at a version of reality that's been, in a way, filtered through the AI's imagination. Okay, I think I see where this is going. So restoration is like carefully cleaning the window while regeneration is like taking a paintbrush and adding your own artistic flourishes to the view. Yeah, that's a great analogy. And that difference, it's absolutely crucial for figuring out if we can trust what we see.
00:08:19:07 - 00:08:43:21
Because if an AI is making up details, even if it looks realistic, it's not evidence anymore. It's like creative storytelling. And that's not what we want in a courtroom or really anywhere where truth matters. No, not at all. And it's not just these fancy AI techniques we need to be wary of. The Hailo article even mentions that some of the traditional image processing methods, the kind we've been using for years, can also introduce issues.
00:08:43:23 - 00:09:01:09
Really? Like what? Well, like, you know, those filters that try to smooth out video and get rid of the graininess. Some of those, like 3D noise reduction filters, they can actually blur out important details along the way. I see, so they're trying to make the video look better, but they might accidentally be hiding crucial evidence in the process.
00:09:01:09 - 00:09:20:04
Right? And then there's things like auto white balance. It's supposed to make sure the colors look natural, but it can also give you a false impression of the lighting conditions at the scene. Okay, so even seemingly simple things can mess with the evidence. That's kind of unsettling, isn't it? Like, how do we know what to trust anymore? Yeah, it is a bit worrying.
00:09:20:06 - 00:09:43:07
But here's the thing—what sets restoration-focused AI apart from these other methods, and even from regeneration, is that it operates within a very specific mathematical framework. Okay, math, I'm listening. So these restoration algorithms, they aim to remove noise or sharpen edges without inventing new information. And because it's all based on these mathematical models, you can actually trace those changes back.
00:09:43:09 - 00:10:03:20
You can still kind of get a mathematical sense of what the original image looked like. So it's like there's a set of rules the AI has to follow, and you can check its work, so to speak. Yeah, that's a good way to think about it. And that's not really possible with regeneration techniques because those are often based on things like, what are they called—variational autoencoders.
00:10:03:22 - 00:10:21:21
Right. And also these stable diffusion models. And these are super complex. They basically learn patterns from tons and tons of data. And then they use those patterns to fill in the blanks in an image. But the way they do it, it's kind of a black box. It's hard to know exactly why the AI is making the changes it's making.
00:10:21:23 - 00:10:46:17
So it's less about following rules and more about making educated guesses based on what it's seen before. Yeah, kind of like that. And those guesses, they might be right sometimes, but they might also be totally wrong. Especially when it comes to details that could be crucial for an investigation. So basically, regenerative AI, it's just too unpredictable, too much of a wild card to be trusted with something as important as forensic evidence.
00:10:46:17 - 00:11:09:21
Right? Because at the end of the day, it's not just improving the image, it's potentially changing the story the image tells. And that's a huge problem for anyone who needs to rely on that image to find out the truth. Absolutely. So this brings up the question: if we know that generative AI can be so problematic for evidence, how do we keep it out of the hands of, you know, investigators or forensic analysts?
00:11:09:23 - 00:11:28:13
How do we make sure they're using tools that stick to the safer restoration methods? That's a really good question. And the Hailo article, they actually point to the hardware manufacturers as having a key role to play in this. Okay, the companies that make the cameras and the computers that process the video—what do they have to do with it?
00:11:28:15 - 00:11:50:03
Well, they're the ones who are ultimately putting these AI capabilities into their products, right? So it's their responsibility to be completely transparent about what kind of AI they're using. Are they using restoration-focused methods or are they using those riskier generative techniques? So it's about giving the people who are actually using these tools—the investigators, the forensic analysts—the power to make informed choices.
00:11:50:03 - 00:12:13:09
They need to know what they're dealing with. Exactly. And if they know that a certain system relies on generative AI, they can choose to avoid using it for anything related to evidence. Yeah, they can opt for systems that are specifically designed for restoration and that have those safeguards in place. Makes sense. But even then, how can we be 100% certain that only those approved restoration methods are being used?
00:12:13:11 - 00:12:40:21
Is there any way to actually, like, guarantee that? That's where these things called secure attestations come in. The Hailo article talks about this. It's a pretty clever idea. Basically, it involves embedding a kind of digital signature—a cryptographic proof—right into the hardware itself. Okay, so it's like a seal of authenticity, but at the hardware level. Right. And this signature, it can only be generated if the AI processing is done using those approved restoration-focused methods.
00:12:40:23 - 00:13:06:00
If someone tries to use generative AI or tamper with the process in any way, that signature won't match up. It's like a built-in lie detector for the AI. Whoa. So it's like the hardware itself is vouching for the integrity of the video. Yeah, exactly. And that's a really big deal for establishing trust, because now you have this extra layer of security—this guarantee that the video hasn't been messed with in a way that could change its meaning as evidence.
00:13:06:01 - 00:13:27:04
And it sounds like Hailo is really taking this seriously, right? Like they're making a point of being up front about how their AI works and making sure their customers know exactly what they're getting. Yeah, the article really emphasizes that. They talk about their commitment to transparency and their promise to clearly indicate in their product documentation when generative AI is involved, which honestly, is how it should be.
00:13:27:04 - 00:13:49:22
You know, they're giving their customers the power to choose. Which brings us back to that key takeaway from the article: AI-enhanced doesn't automatically mean AI-fabricated. There's a big difference, and it's a difference that could make or break a case in court. Couldn't have said it better myself. As AI becomes more and more powerful, understanding this distinction is going to be absolutely crucial—not just for legal professionals, but for everyone.
00:13:50:02 - 00:14:13:04
We need to be savvy consumers of information, especially when it comes to video. So to recap everything we've talked about today: there's a huge difference between AI that tries to restore an image by cleaning up the noise using math and all that, and AI that tries to generate new content based on what it's learned. And while the first one, restoration, can be really helpful for making video evidence clearer...
00:14:13:10 - 00:14:35:07
The second one, regeneration—that's where things get really risky because it's essentially making things up. Exactly. And to make sure we're not being tricked by AI-generated content, it's crucial to look for those hardware-based safeguards like secure attestations. That's how we can be confident that the video we're seeing is a true representation of what actually happened. And this is just the tip of the iceberg, right?
00:14:35:07 - 00:14:58:06
I mean, AI is going to keep evolving. It's going to keep getting more sophisticated. And these questions about authenticity and trust—they're only going to get more complicated. Oh, absolutely. It's an ongoing evolution. But by being aware of these issues, by understanding the technology, and demanding transparency from the companies that are building it, we can navigate this new world of AI-enhanced video responsibly.
00:14:58:12 - 00:15:22:06
And that brings us to the final thought for you, our listener. As AI video enhancement gets more and more powerful—more realistic—how is the whole field of forensic science going to keep up? How will they adapt their methods and standards to make sure that the evidence we're using in courtrooms, in investigations, is still reliable? It's a massive challenge, and one that will have a big impact on how justice is served in the future.
00:15:22:11 - 00:15:28:20
A challenge we all need to be part of for sure. Absolutely. Thanks for joining us for this deep dive, everyone. Until next time.
00:15:28:20 - 00:15:53:20
Thank you for listening to the Hailo AI podcast. If you enjoyed this episode, don't forget to sign up and check out more information at hailo.ai. Keep the conversation going by sharing this with your peers and never stop exploring the future of AI.