S1: All right. So welcome, Michelle. Good to have you back
S1: on unsupervised learning.
S2: Yeah, I'm happy to be here. Thanks, Daniel.
S1: So you're the senior VP of product engineering and data
S1: science at BlackBerry, and you've been on UL before. So
S1: good to have you back. And what I wanted to
S1: talk to you about today is deepfakes. And basically what
S1: you're seeing around that, and I guess starting off like
S1: what are the main cyber threats that you see deepfakes, uh,
S1: picking up for us?
S2: Yeah. You know, I think we're constantly getting immersed with, um,
S2: you know, this intricate dance with innovation and malicious intent.
S2: And I think initially we were seeing that content was
S2: getting generated, whether textual in nature, like better phishing, more
S2: convincing phishing, I would say, or personalized phishing, if you will,
S2: from a content Perspective, trying to reflect your browsing habits.
S2: You know, have a phishing email that would create such
S2: that you would probably click on them. And on that,
S2: I think it's progressively gotten more sophisticated. And, you know,
S2: with media, I think we're seeing, you know, generative AI
S2: that is were used for generating content with the multimodal
S2: model technology. It basically revolutionized. I mean, the idea was
S2: that it was mostly for the entertainment and education industry.
S2: But on the other hand, as we are seeing with
S2: these deepfakes, it's not just limited to phishing and textual, uh,
S2: type of attacks or social engineering attacks, but more powerful
S2: sort of reality, indistinguishable reality from fiction type of attacks where,
S2: you know, deepfakes cause this dystopian vision that is becoming
S2: a reality. Now malicious actors are creating highly convincing Videos, audios,
S2: individuals saying things that they would never say or do before.
S2: Identity theft has been a main fragment of that that
S2: is sort of coming into effect with these deep voice fakes.
S2: So yeah, so I think it sort of started with
S2: the deception and now in a full form of, um,
S2: identity compromise.
S3: Mhm.
S1: Yeah. And when you, when you talk about identity compromise, uh,
S1: what do you mean what type of attack would that
S1: be like. What is the scenario look like.
S2: So um, I think, you know, if you, if you
S2: look at like, you know, um, increasing number of deep fakes,
S2: what we see in, in social media, even things that are, um,
S2: you know, pretty benign people trying to lose a few years, uh,
S2: or trying to lose a few. um, years from their
S2: life in terms of looking more young or, you know,
S2: seeing how they would look if they get older, uh,
S2: even some similar these benign activities, the most concerning development
S2: is the ability to take some of the same technology
S2: and apply on voice and creating, you know, voice cloning, uh,
S2: and voice fakes. And the reason why it is very
S2: disturbing as a trend is because, uh, typically what you hear,
S2: you're you're being trained to like, with all these years is,
S2: is real. Like, if you recognize somebody's voice, we, um,
S2: you know, our, our brains are trained to associate relationship
S2: based on, uh, audio senses. So with deep voice or
S2: voice cloning technology coming out because this enables cyber criminals
S2: to create fake identities, um, you know, that enables people
S2: to disclose information that they would otherwise not pass some biometric. Um,
S2: you know, voice checks and things like that. That's what
S2: I mean by identity. Um, yeah. Cloning?
S1: Yeah, that makes sense. One way I like to think
S1: about this is to imagine, um, when I think about
S1: what can happen from a deepfake. I like to think
S1: less about the deepfake itself and just imagine the impact
S1: that it would have. So, for example, one, uh, one
S1: of the big things is, uh, Beck attacks. And this
S1: is before I. Right, or before modern AI. So it
S1: was like, um, you just send an email and say, hey,
S1: the boss wants you to transfer this money because we're
S1: doing this merger and it's really important. And if the
S1: email was, uh, convincing enough, then that money would would
S1: transfer and they would lose, you know, a thousands of
S1: dollars or millions of dollars or whatever. so that would
S1: be one. Um, and then there's other things like, uh,
S1: you're convinced to vote a certain way, you're convinced to
S1: have a certain opinion, a positive or negative opinion about
S1: a person. So I like to think about the impact
S1: of it and then be like, okay, so how do
S1: we defend against that? Mhm. Um, because there's multiple ways
S1: to trick you into doing something like somebody could just
S1: get on and it's not a deep fake at all.
S1: They just convince you that you should transfer this money
S1: like like oh you should buy this real estate. It's, uh,
S1: you know, it's Oceanside, but somehow it's in the middle
S1: of the country and there's no ocean, but they're just
S1: really good at talking. So they convince you. So it's
S1: like the technique to get you to do the thing
S1: might not be the best place to look for it,
S1: because there's so many of those techniques. The question is
S1: the money transfer, the vote, the, um, opening up access
S1: to an attacker to, like, hey, I need you to
S1: turn on remote access so I can get access in. Well,
S1: that that would be the flag, right? What do you
S1: think about that sort of mental framework?
S2: Yeah, I think, you know, that is a that is
S2: definitely the correct mental framework. I think we're talking about the, um,
S2: the malicious intent largely has not changed. Right. Whether, like
S2: you said, whether it's, you know, you know, getting people
S2: to do something that they would otherwise, not in a
S2: very simple terms. Right. And the speed at which the
S2: act of convincing the speed, if you could map it
S2: to the act of convincing somebody has definitely increased because
S2: of these audio visual, these perceptive sensors, which we believe
S2: as real. What you see is, you know, what the
S2: reality looks like and when that is being questioned on,
S2: you know, that sort of definitely, you know, gets it
S2: to a point where you are now going to be
S2: targeting the different aspects to take advantage of. You talked
S2: about financial, social, uh, defamation, personal attacks, like, you know,
S2: all all of these put together I think is, is
S2: now the landscape that these actors are operating with some
S2: of these technologies. And I think, you know, the most
S2: concerning aspect, I'm convinced that, you know, as the technology evolves, like,
S2: you know, there will always be this cat and mouse.
S2: But the most concerning aspect, I think, of these deepfakes
S2: is the potential for eroding trust. Trust from from systems
S2: that are legitimate, that are that are true. And and
S2: I think, you know, that that is more of these intangible, uh,
S2: effects of this technology, I think.
S1: Yeah. So how do you see these being used in
S1: attack chains? So we already have existing attack chains. How
S1: do you see these being added in or like augmented
S1: with this technology?
S2: Yeah. So I think, you know, we started the discussion with,
S2: you know, phishing. Um, we talked about this disturbing trend
S2: with textual content. Now we're seeing with, with video. Um,
S2: and we're seeing with voice. So voice we already talked about,
S2: for example identity. Identity masquerading, for example, you know, faking
S2: voice identity. We've already seen in the media, for example,
S2: some of these things playing out with millions of dollars
S2: or potentially private information being disclosed. Right. As cyber financial crimes,
S2: for instance. Right. Um, and in video like, you know, uh,
S2: elections coming up in both Canada and United States, you know, uh, this, um,
S2: this disinformation or spread of disinformation at this, at this
S2: speed is changing public opinion visually. Uh, these are sort
S2: of these, uh, Attack vectors, I would say like changing perception,
S2: changing or distorting reality. Um, in, in sense for, for
S2: the mass and also in financial crimes like, you know,
S2: leveraging this technology, uh, to defame brands, uh, create a
S2: direct financial like, you know, you talked about millions of
S2: dollars getting siphoned, uh, creating those. So it's all motivated in,
S2: in those areas. So, um, and we're seeing, you know,
S2: the reality it's no longer hypothetical. Um.
S1: Yeah, absolutely. So there was, uh, one recently with, uh, Ferrari.
S1: I don't know if you saw that one. It was, uh,
S1: it was basically somebody masqueraded the CEO's voice, um, on
S1: a phone call, and they would actually they actually had
S1: done a bunch of stuff on WhatsApp first to get
S1: them to the point of almost doing this thing that
S1: they wanted. And then the final step was, hey, I
S1: need to talk to you on the phone. So it
S1: was Ferrari. So they're Italian, so they have the voice
S1: of the person. They also have the right accent from
S1: the right part of Italy. Mhm. So, um, it was
S1: fairly convincing, but something the executive that they were talking
S1: to and trying to trick something made them question it.
S1: So they asked him a personal question that they knew
S1: about the CEO. Mhm. And the fake CEO the deep
S1: fake couldn't answer. So they ended the call. Yeah. So
S1: so I think um that was a lucky case that
S1: you had somebody who was suspicious. But have you seen
S1: other similar sort of attacks where um, I guess there
S1: was also the one where someone was convincing to send money.
S1: I think they actually did convince them to send.
S2: Money in a British, um, British form. Yeah. Yeah. That's right.
S2: Very one. You know, I think the Ferrari one is
S2: actually a very good case study. Like, I think, you know,
S2: in that one I haven't looked fully into it. But
S2: on the on on the brief, um, articles that I read,
S2: it seemed like the suspicious element was the subtle mechanical, uh,
S2: intonations in the voice that was detected, which sort of
S2: got them into sort of questioning. And, and it's great
S2: for this person to have asked a, um, a shared secret,
S2: if you will, or a previous yes question that they
S2: would have otherwise not known. Um, but you know what? Like,
S2: you know, my first thought reading that was like, uh,
S2: this is real time voice cloning, right? As you're speaking. Uh,
S2: it's sort of generating this. Right? So the compute cycles
S2: that are required, typically for audio latencies to work is
S2: anywhere from 9 to 42 milliseconds for a continuous stream of,
S2: of communication. And this will get progressively better. Like if
S2: it were a static recording that you're playing like a
S2: like a video. Um, that voice overlay or an audio,
S2: for example, uh, you know, uh, or audio voice notes,
S2: for example. Uh, they would be spot on because it
S2: would have had the compute power necessary. And the algorithms
S2: that we have today and that are used by these
S2: threat actors. But I bet that this particular technology of
S2: real time cloning, which is as I'm speaking, it is
S2: sort of, um, transferring the the audio nuances to somebody
S2: else's Mercury would get just better, uh, over time. So
S2: this is quite, quite concerning. But the other one, I think,
S2: you know, there is I came across resemble I or
S2: resembled I forget like, you know, they track quite a
S2: few of these incidents like, you know, worldwide. Um, for
S2: many of these fake like whether it's robocall AI misinformation. Um, and,
S2: you know, I recently read a report from, from Deloitte,
S2: I think the fastest growing forms of adversarial. I, uh, like,
S2: you know, deepfake related are all on financial crimes, like
S2: financial losses. And they're projecting about over 12 north of
S2: 12,000,000,000 in 2023. Uh, which was the case, and 40 billion, um,
S2: by 2027 in aggregate. So which is, you know, is
S2: growing at an astounding rate over, I don't know, like
S2: 12 to 40, like 25, 30%. Yeah. You know, compounding rate. Um,
S2: and you know, in one of those reports, you know,
S2: especially for the financial crimes Deloitte reported, these fakes are proliferating, um,
S2: mostly in the banking and financial services as being the
S2: main target. Hmm.
S1: Interesting. So as the stuff gets better and like you said,
S1: it becomes indistinguishable. Like there's no way to tell the difference. Mhm. Um,
S1: so one one thing is the voice sounds better, but
S1: what I feel like if there's extra context the more
S1: the attacker knows about the thing. So imagine this is
S1: a fully automated AI attack. That's even worse. So it's
S1: not even a voice a real time clone of a person.
S1: It's actually like an AI agent that's just calling and
S1: trying to get the things to happen. But but it
S1: has been given a full database about everything about you
S1: or about me or about this, uh, Italian executive. So
S1: it knows, like, the name of their dog because it
S1: got it from, like, Instagram. Right? And it's got all
S1: this personal data about like, it knows the wife's name
S1: and everything or whatever. So how how do you defend
S1: against that? So let's say it's a perfect voice. It's
S1: perfectly real time, but it also has deep knowledge from
S1: open source intelligence about the actual perpetrator.
S4: Yeah, yeah.
S2: No, I think, you know, it's, uh, as you're saying, right?
S2: I mean, this type of, uh, generative AI, adversarial AI,
S2: you know, it creates new attack vectors with all of
S2: this information, multimodal information that no one sees coming, and
S2: it creates a more complex, nuanced threat landscape, um, that,
S2: you know, prioritizes identity driven attacks, and it'll only get better. Um,
S2: so in the short term, the way I think about
S2: this and most companies that that I talk to their
S2: CISOs is like training, right? The first thing is at
S2: least deepfake detection training, which is recognizing inconsistencies in facial expression, uh,
S2: audio quality or video quality? Uh, before disclosing sensitive information, uh,
S2: validate using a verification protocol, uh, of contacting them through
S2: known means, like, for example, through their, uh, known contacts,
S2: like phone or ask them questions like you talked about
S2: this shared secret, like ask them something that you know
S2: they would otherwise have not shared or disclosed. Uh, create
S2: these these validation, uh, protocols, um, and have people be
S2: more aware of social engineering from, from an awareness perspective. Right. Um,
S2: I mean, that's the, you know, that's just to get
S2: get to the short term problem just with exposure and training, really.
S2: But on a more broader term, I think social engineering awareness, uh,
S2: basically should should drive these regulatory verification process. I think
S2: the governments have have a part to play here to
S2: make content provenance or identity? Um, you know, spoofing, using AI, uh,
S2: as a mechanism, as a mandatory safeguarding, like in Canada,
S2: for example, I can say, uh, most of the provinces
S2: have enacted legislation on sharing non-consensual media, for example. So
S2: the acts are already there, uh, in identity fakes, for example.
S2: But that needs to be put on AI. Um, the no.
S2: AI Fraud Act, uh, that was introduced in the US
S2: House of Representatives, I think, uh, earlier this year I
S2: believe is a good first step. Right. Uh, fraud itself.
S2: There are regulations around that. There's laws around it. We
S2: just need the governments to to catch up to the
S2: level at which the technology is progressing and, and create
S2: it within, within the AI framework. Um, yeah.
S1: I I'm sorry. Go ahead.
S2: No, no, I was just saying that, you know, training
S2: and then the regulatory criteria. But there are things in
S2: the technology side as well that we can do. But perhaps,
S2: you know, the immediately, uh, there are these two things
S2: that come to mind.
S1: Yeah. Well, one thing I worry about there is that
S1: I agree that government is going to get involved and
S1: should get involved, but I think about spam calls in
S1: the US. I don't know about Canada in the US.
S1: It's still very bad. Um, I basically have an allow list. Mhm. Um,
S1: and all other calls just go directly to voicemail because
S1: I couldn't handle it any other way. Mhm. Um spam
S1: calls are already illegal. Fraud is already illegal. So I
S1: if the government says it's illegal to do fraud with I,
S1: if it's already illegal to do fraud, I'm not sure
S1: what exactly. Like who would be willing to do the
S1: fraud without I. Mhm. Even though it's illegal. But once
S1: the new law came out they would be like oh
S1: well now it's illegal with AI so I'm not going
S1: to do it.
S3: Mhm. Yeah.
S2: No I think you know and that's where so, so
S2: I think the that's a great point. So the first
S2: thing is you know educate. Right. We all need to
S2: be aware that reality is getting distorted and be aware
S2: of our surroundings. Yes. Number two we do need some
S2: guardrails regulatory guardrails. Right. So that, you know, at least
S2: it it puts some checks and balances. Or if somebody
S2: were to to file a complaint, there is a legal
S2: framework to act upon. So today for example, if I
S2: went and said something like this happened. Um, the legal
S2: framework is not there to support, uh, let's say the, the,
S2: the legal proceedings that would follow this.
S3: Yeah, that's a good point.
S2: Yeah. But also there is a technology side to it, right. Like,
S2: I mean, if you take a step back and if
S2: you think about it, look it will get progressively better.
S2: And why do I think about it that way? because,
S2: you know, this branch of generative AI, you know, works
S2: in a way like like, you know, we talked about this,
S2: I think, in our previous discussion about adversarial networks. Right.
S2: Adversarial networks, Gans like or or various types of autoencoders,
S2: for example. Typically, the way it works is like you
S2: have two pairs of network, right? One, you know, deep
S2: neural network is called the generator that's generating this content.
S2: And the other one is a discriminator and it's critiquing
S2: the content. Simplest way to think about that. So the
S2: generator will get better and better as the discriminator gets
S2: better at critiquing critiquing the generation. Right. Yeah. Zero sum game.
S2: So the very technology that is sort of enabling to
S2: detect and identifying what is a fake and what is
S2: real in itself is assisting in getting better at the content.
S2: So that's why, you know, I think about this as,
S2: as the need to accelerate the framework, the regulatory framework
S2: for authenticity detection technology. So accelerate the innovation on cryptographically
S2: securing generative AI content through fingerprint.
S1: Okay. I like that.
S2: Simplicity. You know, the government regulatory criteria can come in
S2: and enforce the authenticity. Like we all need to learn
S2: how to verify you know, what is valid and what
S2: is authentic content. Just look at how we learned how
S2: to check if a browser is safe, right? There is
S2: a little.
S3: Um yep.
S2: Lock at the left hand side. Oh, okay. It's safe. Right?
S2: SSL and all that. Right. The same way we need
S2: to sort of, you know, do the same thing to
S2: learn how to check if a content media is safe. And,
S2: you know, we consume our content through browsers. Right. I mean,
S2: all of this thing is manifesting itself in the very
S2: vehicle that's delivering it is are these browsers. So there.
S3: Is a way and.
S1: Mobile apps and mobile apps.
S3: And mobile.
S2: Apps. But at the end of the day, there is
S2: a way and and we consume most of the content
S2: using these sort of instruments. So we can see how
S2: I'm drawing these parallels, that if we sort of accelerate
S2: the innovation on cryptographically security, securing and validating these, these
S2: fingerprinted content, then we should be able to tackle this technology, right?
S2: I mean.
S1: So I like that. Yeah. Is it. So it's when
S1: you're saying legislation you're not talking about make it illegal
S1: to do bad things because it's already. No no.
S3: No. Yeah. Yeah.
S1: You're talking about requiring, uh, technology providers to have an
S1: authenticity mechanism. It's really funny you say that because I
S1: was going to bring up a similar point. So, um,
S1: we're in zoom right now. You see, when I'm talking,
S1: you see, I have a green outline around. Mhm. When
S1: you talk the you have a green outline. And in
S1: this case that's to indicate that that's person is talking. Right.
S1: Obviously however I've been thinking about exactly what you said.
S1: Which is, um, what if Apple and Android and YouTube
S1: and all of meta, they had a mechanism where, um,
S1: so let's say I'm talking to you on the phone. Mhm.
S1: When I initiate my phone call to you. Mhm. Um,
S1: it's using my secure enclave on my phone. And so
S1: it's verifying that I got in with my face or
S1: my finger or um touch ID or whatever.
S2: Authenticity. Yeah.
S1: Yeah. So it authenticates that. And then when the call
S1: comes over to you, you see something, you see a
S1: blue outline, you see a green outline, you see a
S1: check mark, just like you said with the lock symbol.
S1: So now when we're having a conversation, it's validated. So
S1: same same with this. Like you said, we could do
S1: you could do a real time deepfake pretty soon. So
S1: it looks like I'm talking to shell, but it's not
S1: actually you. But if there was a green outline on
S1: a check mark, that would mean that some combination of
S1: my operating system and zoom had validated and continued to validate.
S1: So maybe in the middle of our conversation, we both
S1: get prompts because we've been talking for ten minutes. We
S1: have to re authenticate the feed. Mhm.
S3: Mhm.
S2: So yeah you know you you're spot on. And you
S2: know I almost think of this as a joint government
S2: and industry partnership. Like you know I'm often I have
S2: the opinion that this is definitely a solvable problem. Yeah.
S2: And as an industry thought leaders we have to establish
S2: what this common identity assertion protocol is and standardize that.
S2: And then all of these companies that are in the
S2: business of of creating media or transmitting media or exchanging media,
S2: sort of, you know, um, adhere by it. Like, I mean,
S2: you know, I'm, I'm actually quite, um, encouraged to see
S2: the DARPA very recently, uh, to deal with at least the, uh,
S2: face swapping technology and the puppeteering technology, which which is
S2: also a phenomenally interesting branch of generative models is where
S2: your expressions you are still the same person, your identity
S2: is not solved, but your expressions are. But to deal
S2: with that, they initiated a new research, I think called
S2: the Media Forensic Research Acceleration Program R&D program, if you will,
S2: to identify fake digital visual media detection method. Right. Um,
S2: and that will tackle, you know, those sort of things.
S2: But I think in order to deal with the identity
S2: side of things or validating, I think, you know, what
S2: what you're suggesting is, is a great way of of
S2: tackling that. In fact, I was recently reading a paper
S2: like there is this cryptographic mechanism called um, uh, zero
S2: knowledge proof. And the idea is, is quite simple. It's basically, um,
S2: there is something that both you and I know without
S2: you disclosing what you know, I'm able to verify, uh,
S2: whether your claims are true or not.
S1: Almost like Diffie. Hellman.
S2: Yeah, except in Diffie. Hellman. Yeah, yeah, except that there is.
S1: But but, um. But the middle person doesn't get to
S1: see the thing exchanged.
S2: Exactly. Yeah. And this zero knowledge proof is, is actually
S2: applied in many different places. It's not new, but what
S2: is new here is the application of zero knowledge proof
S2: in authenticating and privacy maintaining hardware. Like, um, you know,
S2: I came across a company that's sort of dabbling with
S2: this called snark. They're using zero knowledge, zero knowledge proof microphones,
S2: which you know, can prove the audio was indeed recorded
S2: in that thing. Some media companies like Canon and Nikon
S2: are dabbling with zero knowledge imaging technology, whereby they can
S2: ascertain that this was actually captured using light rays coming
S2: in a camera lens.
S3: Oh, interesting.
S2: And or if it has been edited in a particular way.
S2: But this whole audio visual industry coalition content provenance, authenticity
S2: is a serious topic. And it is time that, you know,
S2: we we find ways to certify, uh, source of digital content,
S2: how it was generated. Um, and things that you just
S2: talked about ascertain before these gadgets, like mobile devices and
S2: other such things are used to validate these things right now,
S2: if you think about it. Right. Privacy, uh, or identity
S2: verification is only used for content that you own. Like
S2: for example, my phone, for example, is going to ask
S2: me for my password and a bunch of other things
S2: before it shows me my stuff, right? Or opens. But
S2: that has no bearing when I'm calling you, for example.
S2: Like you have no idea. Right. So so this this
S2: thing is, as you mentioned, is actually now very important
S2: is all of this identity validation that happened on the phone.
S2: This trust needs to be expanded into the other entity
S2: that you're interacting with. So the trust network needs to be, um,
S2: shared using whatever methodology that these companies choose. But yeah,
S2: our standardized.
S1: Yeah. So I think that's correct. Um, I'm also thinking
S1: about a thing that you said earlier when you mentioned Puppeting. Um, yeah.
S1: I didn't know there was a name for this, but
S1: I think it might be the same thing I'm thinking of.
S1: So the the avatar on the other side looks like
S1: this young, like.
S3: Um.
S1: Like, uh, almost like anime looking, uh, influencer girl. And she's, like,
S1: really animated and, you know, pretty and everything, and she's
S1: talking about whatever the topic is. And then you see
S1: right next to it, it's actually like a 47 year
S1: old male, and he's the one actually doing all of
S1: the emoting and everything. And, um, it is real time
S1: face swapping and real time hand swapping, uh, costume clothes, everything.
S1: So that raises an interesting point. Uh, based on everything
S1: we talked about.
S3: At.
S1: The start of the call, um, there was authentication that happened.
S1: So they got a green box. Um, but then this
S1: technology is now on. So now it looks like this
S1: other person. Um, so what? This just got me thinking,
S1: and I hadn't thought of this before. You know, this
S1: reminds me of, uh, gaming situations where, um, games, there's
S1: so much hacking happening in games that, uh, a lot
S1: of game vendors switch to basically having to run a rootkit.
S3: Mhm.
S1: So they need end to end, top to bottom deep
S1: kernel implementation to know that you do not have some
S1: sort of shiv. Yeah. Some sort of injection capability inside
S1: of the thing. And it's looking at all the processes
S1: that are running. It's looking for evidence of malware. It's
S1: looking for evidence of tampering. So, so the question is
S1: like if we start a video call and then I
S1: start software like that. Mhm. That technology needs to be
S1: able to know that I'm using the puppet technology and
S1: that there's an interception and translation happening.
S3: Correct.
S2: Yeah. And I think you know that's a that's a
S2: very well uh explained kind of Uh, thought process. And
S2: that's also one of the reasons why I think it's
S2: not just that. And I talked about this zero knowledge
S2: proof mechanism for authenticity built into the hardware. Um, because,
S2: you see, if my camera is showing my video and
S2: if the camera in the live stream, this hardware authenticates
S2: that the video stream is basically what it is processing
S2: using the, uh, the ZK hardware, uh, research I talked about.
S2: Then any cross stream or stream mixing in the middle, um,
S2: the receiving software should be able to validate the authenticity
S2: that this is not what the camera captured. Right? Yes.
S2: And that is the key, right? It you know, it's
S2: not just at one level. It has to be that
S2: the trust has to go all the way from the
S2: physical level. Right? The lights and everything around here. To
S2: what the camera sensor captures, to what the media gets digitized.
S2: So just trying to tackle that the digital media layer
S2: is insufficient. It needs to have the analog. Um, it
S2: needs to have the analog ancillary to also transport this
S2: authenticity and validation mechanism back for for real time communication,
S2: whether it's an audio microphone or a video camera or sensor. Yeah.
S1: Yeah. I love what you're saying there, because I love
S1: the fact that the hardware itself is involved. So to
S1: your point. Canon. Canon. Canon and Nikon. So it's almost
S1: like they would have their own version of like a
S1: secure enclave or something similar where it's like, that's a
S1: protected system. It's the one doing the signing at the
S1: at the camera hardware level, which is part of a
S1: later signature which is passed on. That's right. So it's
S1: like this chain of custody where it's an unbroken thing.
S3: Yeah.
S2: But the challenge with that, Daniel, is that, you see,
S2: right now, the fragmentation in this space is going to
S2: be devastating. Like, that's the worst thing that can happen. Yeah. Fragmentation.
S2: Meaning that okay, one person is doing or one company
S2: doing it this way. The other company is doing it
S2: that way. And there is no like, you know, um,
S2: so so that's why I think it's very important that
S2: the Logitech camera can interact with some of my phone camera,
S2: for example, or, you know, the interoperability of this. So like,
S2: imagine if your web browser did something different, uh, of
S2: SSL and something else did something different. It's going to
S2: be just a chaos. So standardization of this mechanism to
S2: tackle deep fake authenticity of of digital media, whether it's
S2: stored media or media in transit.
S3: Um, you know.
S1: Now that I'm thinking about this, I think you're right
S1: about that, because I think what will probably happen is
S1: the what we will agree on is we agree to
S1: trust zoom and then zoom on each of our sides.
S1: Does the camera validation because the camera got some sort
S1: of certification from somebody like Apple or Mac OS or windows? Correct.
S1: So zoom trust the camera. Therefore zoom signs it. Therefore
S1: your side agrees because zoom side zoom signed both sides.
S3: Sure. Something like that.
S2: That works. That works too. And yeah, that's you know,
S2: that that is a kind of standardization. But I was
S2: going a little bit broader. I was saying that we
S2: should like, we should almost go to the layers of
S2: network communication, the same way how we communicate with streams
S2: like we do. Like you talked about Diffie-Hellman, I'm talking about,
S2: you know, um, stream establishment at the internet protocols. Authenticity.
S3: Oh, sure.
S2: So I think it's time for us to look at, like.
S2: I mean, we can keep patching this stuff, right? We
S2: can we can keep creating these glues and, you know,
S2: but I think it's time to to take a step
S2: further and start, um, you know, um, the contracts of
S2: this authenticity of the hardware, the data, like the same
S2: way how we digitize the data. We need to embed
S2: some of these validation mechanisms right into the protocol.
S1: You know, honestly, we should, um, not not perfectly on topic,
S1: but we should actually collaborate on this because, um, I
S1: don't think it's going to be easy for a small
S1: company to do this. I think this is really going
S1: to be like a consortium. Mhm. Um, but I used
S1: to be at Apple. Um, I still know a lot
S1: of people over there. I know a lot of people
S1: are thinking about this, but I am very surprised that
S1: I have not heard more people talk about what you
S1: just said. So for example, um, IPsec, uh, Randall, like
S1: all the fundamental protocols, uh, the fundamental algorithms, what is
S1: an underlying base standard like TCP, IP, like TLS? Um, um,
S1: you know, is it, uh, are we doing public key
S1: for the exchange? Are we doing symmetric for the for the, uh,
S1: the communication? Yeah. So it's like all those things need
S1: to be considered and built into like, like you said,
S1: a fundamental protocol which includes the authentication piece, which includes
S1: the re prompting for authentication over certain periods of time
S1: based on, uh, so for example, here would be a
S1: great like method for the, uh, thing. Uh, you have
S1: a policy established during the, the initiation of the call
S1: so that if certain things are being talked about. It
S1: up levels the requirements so it prompts you both sides
S1: more often.
S3: Mhm. Mhm.
S1: For revalidation. Yeah. Things like that.
S2: Yeah absolutely. And you know you talk about Apple and
S2: I think it's interesting right. Apple is in a unique
S2: place to to really solve deepfakes is because they have
S2: a full control of end to end ecosystem if you will.
S3: Yes.
S2: Um, all the way from the hardware to the content
S2: to the method of that content distributes, and they have
S2: statistically significant density of communities that interact with those content. Um, so,
S2: so that, that, you know, that's one aspect. And the
S2: other aspect is I think, you know, if you look
S2: at the rate at which the technology is evolving, um,
S2: deep fakes are probably Significantly impacting our ability of what
S2: reality looks like and or eroding trust from systems.
S3: Yep.
S2: And that is massively concerning.
S1: Yeah I agree. Yeah. One thing I just realized is, um,
S1: I would love like a little I think this is
S1: probably coming soon with AI agents. So you have like
S1: a little bot that is watching this chat. And one
S1: of the things it would have reported is, um, Shil's
S1: background looks like a real background that is blurred. Daniel's
S1: background looks to be AI generated. So I, I'm watching
S1: him very carefully to make sure he doesn't have six
S1: fingers or something. You know what I mean? So you
S1: could just have an alert that's like right off the
S1: start before we even started. It's a fake background.
S3: Mhm.
S1: You know what I mean.
S3: Yeah. No.
S2: Absolutely. Yeah. I think, um, I think there are various
S2: ways to solve this, but, you know, um, there are
S2: things that can be done in the short term. There
S2: are things that can be done in the mid-term. But
S2: I think, you know, if we're talking about thought leadership,
S2: vision as to where we're going, I think it's time
S2: for us to kind of, you know, uh, rethink what
S2: we are doing and how we're going to deal with
S2: fakes in general. Digital fakes. AI is helping it make better.
S3: But yeah, yeah, yeah, yeah.
S1: I think the way you're talking about it is exactly correct.
S1: Ultimately it's a trust issue. So anything that is eroding
S1: that trust is really the problem. And that's where we start.
S3: Exactly.
S1: And then we start with that trust problem. And then
S1: you start thinking about a trust protocol a more fundamental
S1: technology protocol like TCP, IP, like HTTP, something, you know,
S1: at a deeper, more fundamental.
S3: Yeah, yeah.
S2: And you know, Daniel, there is also another important thing here. Like,
S2: you know, some of these things were developed for entertainment purposes. Like,
S2: if you think about it, the very premise. Right. If
S2: you go look up in GitHub and you search for, uh, FS, uh, Gann, uh,
S2: facial expression and you'll see like incredible research papers and
S2: then implementation of them. And they are majority uh, the
S2: goal is to demonstrate what the technology is capable of.
S2: Some of the first applications were for fun and. Yeah.
S2: So what like, you know, I sent a picture or
S2: video that looks, uh, five, ten years, um, you know,
S2: of my age taken off, right? As long as I
S2: do not claim, I think it's perfectly fine. As long
S2: as they say, hey, you know, look, this is. And
S2: there is no claims made that this is who I
S2: am or this is what it is. The problem becomes
S2: when some like, you know. So the root of the
S2: problem is, is a fake, whether it's deep or not,
S2: I think is is it or AI generated is a
S2: different point altogether?
S3: Yeah.
S1: No, I think that's right. It's it's a great point
S1: because there's a harmless removal of 15 years of age. Mhm.
S1: But if it's a guy and he's trying to get
S1: a model, uh, modeling job and the modeling company stands
S1: to lose money from this contract being signed, now that
S1: innocent thing is no longer innocent.
S3: Exactly.
S2: Yeah. Exactly. Which is why, you know, the technology is
S2: just enabling. And that's why my points were like, we
S2: need to find a way to deal with the technology. Mhm.
S1: Yeah. So any any tips for people to learn more
S1: about this.
S2: Yeah I think you know um like we recently did
S2: like the threat research team and the and the data
S2: science team, uh, did some work to, to publish this um,
S2: thing from BlackBerry about deep fakes. I encourage people to
S2: read it. I think they're going to find it informative.
S2: It's developed in a language that is very easy to understand,
S2: and I think right now I would encourage people to
S2: sort of learn about these things of what's possible. Right.
S2: That's the first thing that at least you're skeptical when
S2: you see something or your antennas kind of pick up
S2: something that that you might otherwise might not have. So
S2: awareness I think, is is the key at this time.
S1: Okay. Yeah, we'll definitely put the link to that in
S1: the show notes. Um, any predictions for like the next
S1: year or 2 or 3 years?
S2: Um, well, I think this technology is going to get
S2: progressively better. You're going to see more hyper realistic content.
S2: In fact, you're going to start seeing full body, not
S2: just faces and expression puppeteering. I think you're going to see,
S2: you know, hyper realistic content. You're going to see content
S2: interacting with other content in social settings. You're going to
S2: see more personalized attacks through this mechanism. Uh, you know,
S2: public figures or people you dislike. You're going to be
S2: able to start propaganda and the availability of these tools like,
S2: I mean, from $5 to $15 a month. From a
S2: subscription perspective, you can create some of this stuff, uh,
S2: with a bit of programming. You can go download these
S2: GitHub projects and do your own, if you will. Um,
S2: you know, the like, you know, the possibility is limitless.
S2: So deepfake as a technology will continue to evolve because
S2: it does stoke a, a, uh, in a reason for
S2: why we do certain things that, that are, uh, not
S2: the best moral grounds, if you will. So it will
S2: get become more sophisticated, harder to detect the very technology
S2: that is required to do this. Um, is is going
S2: to basically enable this, this growth. And the challenge will
S2: be there in the coming years unless we as a
S2: community do something about it.
S1: Yeah. So the better that stuff gets, the more we're
S1: going to need the types of controls that you talked about.
S3: Exactly.
S2: Yeah, absolutely.
S1: Where can we learn more about you and your team
S1: and the work that you're doing?
S2: Uh, we, uh. That's great. Like, you know, we have
S2: a data science research blog where we publish, uh, things
S2: that we learn, um, time to time, uh, at BlackBerry, um,
S2: papers that we publish. Um, so, so I welcome, uh,
S2: people reaching out if they want. Uh, I always love
S2: to have a great conversation. Some of these conversations we
S2: had were very insightful. Um, yeah.
S1: Okay. Well, awesome. Well, it's great to have you back. And, uh,
S1: great conversation, as always. I appreciate the time.
S2: Hey, thanks a lot, Daniel. Thanks.
S1: All right.
S3: Take care. Bye.