Identity fraud has evolved. A fake voice or a doctored document is no longer enough: today, the threat has a face, an expression, and eye contact. Generative AI has enabled a new generation of hyper-realistic synthetic videos that bypass traditional verification systems, making the fake look real and dangerously convincing.
The rise of synthetic videos and their direct impact on identity fraud
Until recently, creating a convincing fake video of a person required technical expertise, time, and resources. Today, accessible tools like Google’s Veo 3, OpenAI’s Sora, and Synthesia allow anyone to generate hyper-realistic videos from a simple text prompt. This has opened the door to a new type of fraud: identity impersonation through deepfakes.
Imagine receiving a video from a senior executive, a relative, or a client, seemingly live, urging you to take immediate action. This scenario is no longer fiction. It’s an increasingly common reality that challenges digital trust at its core.
The real threat of digital fraud through AI-generated videos
AI-generated videos are no longer just viral curiosities. Their use in identity fraud is real and expanding rapidly:
- They visually impersonate real people, executives, employees, customers, or family members.
- They deceive video-based identity verification systems, remote onboarding, and biometric authentication.
- They power advanced social engineering attacks, triggering wire transfers or changes in internal access rights.
Combined with leaked personal data, these videos can deliver hyper-personalized messages that trick victims into believing they’re speaking with someone they trust. The line between real and fake is disappearing.
Why are they so hard to detect?
The realism of these videos is improving at an alarming rate, surpassing many existing detection methods:
- They feature realistic eye movement, facial expressions, and voice synchronization.
- They can be used in real-time video calls via spoofing platforms.
- They’re accessible to virtually anyone, no technical background required.
This level of sophistication demands next-generation solutions to ensure security.
How Facephi stops synthetic video fraud
While deepfakes may fool the human eye, there are invisible signals that can expose them — with the right technology. For example, imagine an employee receives an urgent video call from someone posing as a company executive, requesting an immediate transfer of funds. Even if the video appears authentic, Facephi’s solutions detect invisible anomalies and block the transaction before the fraud can occur.
At Facephi, we’ve developed a multi-layered detection strategy that goes beyond the image:
- Passive liveness detection: it detects whether a real person is interacting in real time, using technology that analyzes cues such as natural facial lighting or microexpressions to confirm it’s not a pre-recorded video—without requiring any explicit user actions.
- Facial biometric recognition: analyzes unique facial features that are extremely difficult to replicate in synthetic videos.
- Document verification with morphological analysis: validates not just the content, but the physical structure of documents accompanying the video.
- Synthetic content detection: uses trained algorithms to identify artificial faces, artifacts, and temporal inconsistencies.
- Behavioral biometrics: even if the video seems authentic, how a user types, moves the cursor, or interacts with their device can reveal whether they’re genuine or not.
- Secure communication channel: protects the interaction from origin to destination, preventing real-time tampering or spoofing.
This comprehensive approach allows us to detect visually undetectable impersonation attempts and stop fraud before it happens.
Digital identity verification in the age of synthetic video
Identity theft through videos generated with generative artificial intelligence is no longer a theoretical scenario or a future threat. These techniques are already within reach of any malicious actor with access to open-source tools capable of producing highly convincing videos in a matter of minutes.
That’s why traditional solutions are no longer enough. Today, identity verification requires a robust anti-fraud architecture that integrates real-time detection, morphological analysis, digital behavior monitoring, and active channel protection.
At Facephi, we understand that authenticity can’t rely solely on what’s visible. Our solutions analyze invisible signals and complex patterns that can only be generated by a legitimate human being.
While many solutions focus exclusively on the visual analysis of the video, Facephi takes a holistic approach that combines biometric detection, behavioral signals, and channel security. This technological differentiation allows our solutions to detect threats that other systems overlook—providing stronger and more effective protection against deepfakes and synthetic identity fraud.
Protecting digital identity beyond appearances
Tools like Sora, Veo, and Synthesia are radically transforming digital content creation. But with that power comes a new responsibility: protecting digital identity against synthetic threats.
At Facephi, we are committed to ensuring that every person can verify their identity in a secure environment, even against the most sophisticated generative AI attacks.
We continue to innovate so your customers’ identities are protected beyond what the eye can see, with technology capable of detecting what even the most realistic videos try to hide.
Want to protect your organization against synthetic media threats?
Request a personalized demo or explore our use cases and discover how we can help you strengthen your digital identity verification strategy.