Can AI and Blockchain Help Fight Streaming Piracy?
Learn more about streaming piracy and content protection at Streaming Media's next event.
Watch the complete presentation at Streaming Media East, B204. Batten Down the Hatches: Stream Piracy and How to Protect Your Content, in the Streaming Media Conference Video Portal.
Read the complete transcript of this clip:
Christopher Sass: You gotta be careful with AI, right? 'Cause it's a buzzword. It's like saying "Cloud," right? So I see today a lot more machine learning and kind of more where there are humans involved in directing it and a lot of learning and things, like you said, behaviors change, right? You can do really interesting things if you understand the pirate's ecosystem, if you look at the temperature of boxes and where they're on at a time and records, you can figure out a whole lot of stuff, but you really need to interpret and make that valuable. So I see a lot of investment there. I do see things coming with the streaming and flow analysis, a lot of that kind of technology's really emerging. That's pretty exciting stuff. And there's a few startups out there doing things with blockchain. You talked about "what if you could just buy a piece of content, and you only wanted the piece of content?" Well, if I had a public immutable chain of custody basically of the content, I could do that. I could now take my piece of content and let you buy this or buy that, put it in a blockchain, and I know exactly where it went. That's really early. You know I think, if you say what's two, three, five years out, that, we're kind of in the infancy, there's a few companies out there, I don't know if we're quite ready for primetime with it. I know that even our R&D people look at those kind of things, but we're probably not productizing that stuff right now, it's more just that's kind of interesting, it's got some legs, let's figure out where it goes.
Matt Tooley: And you know, one thing that we've been looking at with the companies is how to apply a machine learn and supervised machine learning cause there's a lot of technology doing that in the cyberspace for identifying malware, and so if we're, we're just saying "Okay, just change the word malware to video piracy and start, can you find it?" Well, it turns out you can. So we're working with companies to see if we can get that productized as a way to be able to sniff it right out on the fly.
Jason Thibeault: Yeah, I think there's definitely still a lot of human intervention needed for data analysis. I mean, when I talk to one of our member companies-- big, big, big content owner-- and you know he was telling me that they really look at logs, and they have thresholds for logins, for example, and they see someone logging in a lot of times from a lot of different places, hmm, maybe something, somebody needs to reach out to that user. You know, maybe we'll just cut their account off and let them contact us, so if they're a pirate, they're not gonna contact 'em right? They're just gonna be like "Ah, I got caught, I'll go somewhere else." But if they're a legitimate user, whose credentials might've been compromised or stolen, the user will reach back out and go "Hey, why'd you cut off my account?" Well, we noticed this and we'll light you up again, change your password, and then if it happens again, well they probably know that was a bad actor who was just being, you know--
Christoper Sass: And that's the big data angle, right? I mean that's like with credential sharing we do that. That's why say you get scores, and one of the sore things is like you come in and say "Hey, we just locked your account, we want you to call us to turn it back on." Or now you gotta use two-factor authentication-- yeah, that's inconvenient for the normal user. But if you're a bad actor and you're doing something illegal, so what if it's inconvenient? The operator shouldn't care, but we wanna have a high degree of confidence, and I think the key there is, with big data, it doesn't solve all problems. You need a really specific problem set, and you need to be able to take the noise out of the data, and that's where the human comes in, 'cause there's just too much data. You really need to figure out what's valuable data, and that's where we're kind of in the early stages. 'Cause I don't know that for this application that a lot of companies are there yet.
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