Christopher Ren does a solid Elon Musk impression. Ren is a product manager at Reality Defender, a company that makes tools to combat AI disinformation. During a video call last week, I watched him use a single photo to generate a simplistic deepfake of Elon Musk that maps onto his own face.
This digital impersonation was to demonstrate how the startup’s new AI detection tool could work. As Ren masqueraded as Musk on our video chat, still frames from the call were actively sent over to Reality Defender’s custom model for analysis, and the company’s widget on the screen alerted me to the fact that I was likely looking at an AI-generated image and not the real Elon. While I never really thought we were on a video call with Musk, the demonstration highlighted the very real problem of deepfakes.
Real-time video deepfakes are a growing threat for governments, businesses, and individuals. Recently, the chairman of the US Senate Committee on Foreign Relations mistakenly took a video call with a deepfake. An international engineering company also fell victim to a deepfake video call earlier in 2024.
These incidents underscore the potential for such technology to be used in fraudulent activities. “It’s probably only a matter of months before we’re going to start seeing an explosion of deepfake video, face-to-face fraud,” says Ben Colman, CEO and cofounder of Reality Defender. When it comes to video calls, especially in high-stakes situations, seeing should not be believing.
The startup is focused on partnering with business and government clients to help thwart AI-powered deepfakes. Reality Defender’s plan for the real-time detector is to start with a plug-in for Zoom that can make active predictions about whether others on a video call are real or AI-powered impersonations. The company is currently working on benchmarking the tool to determine how accurately it discerns real video participants from fake ones.
Detecting deepfakes in real-time
For now, the new software feature will only be available in beta for some of the startup’s clients. This is not the first time a tech company has shared plans to help spot real-time deepfakes.
In 2022, Intel debuted its FakeCatcher, designed to analyze changes in a face’s blood flow to determine whether a video participant is real. Intel’s tool is also not yet publicly available. Academic researchers are exploring different approaches to address this specific kind of deepfake threat as well.
“These systems are becoming so sophisticated to create deepfakes. We need even less data now,” says Govind Mittal, a computer science PhD candidate at New York University. “If I have 10 pictures of me on Instagram, somebody can take that and use it to create a deepfake.” Real-time deepfakes are no longer limited to billionaires, public figures, or those with extensive online presences.
Mittal’s research at NYU proposes a potential solution to blocking AI-bots from video calls, where participants would have to pass a kind of video CAPTCHA test before joining. As Reality Defender works to improve the detection accuracy of its models, Colman says that access to more data is a critical challenge to overcome. He’s hopeful that more partnerships will help fill these gaps.
What can you do right now to protect yourself from video call scams? Not getting overconfident about your ability to spot video deepfakes is crucial to avoid being scammed. The technology in this space continues to evolve rapidly, and any telltale signs you rely on now to spot AI deepfakes may not be as dependable with future upgrades to underlying models.
“We don’t ask my 80-year-old mother to flag ransomware in an email,” says Colman. “Because she’s not a computer science expert.” In the future, it’s possible real-time video authentication, if AI detection continues to improve and becomes reliably accurate, will be as taken for granted as that malware scanner quietly working in the background of your email inbox.
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