Solutions
Meeting deepfake protection
Continuous verification of who is really on the call — powered by Halo.
$25M
stolen in a single deepfaked video call — every 'participant' but the victim was syntheticSource: Arup case, 2024
- On-device detection during the call
- Faces and backgrounds verified
- A risk signal the moment something's synthetic
The threat
One synthetic participant on one video call can move millions. Deepfaked vendors, cloned colleagues, and replayed backgrounds are already inside enterprise meetings.
What Halo does during the call
The check runs the whole way through the meeting, on the participant's device, not just at the door.
Halo verifies every face and background with Eva V1.6, and raises a risk signal the moment something turns synthetic.
Verified at join is not verified at minute forty
A call is live: a participant can hand off, an account can be compromised, or a synthetic face can switch on right when the meeting turns to money. Halo holds the result for the whole call, not just the join.
/ROI
What this is worth
- Loss avoidance: as an illustration, at 50,000 checks a month, catching just 0.2% more synthetic media is ~100 frauds stopped — at a $10k average loss, that's ~$1M a month that never walks out the door.
- Review time: evidence-backed results cut manual review from minutes to seconds, so analysts handle the small flagged fraction instead of screening everything.
- One integration: a single REST API covers faces, documents, and devices — no second vendor, no second review queue.
Common questions
How can you tell if someone on a video call is a deepfake?
Human tells — odd blinking, blurring at the edge of the face, lag between expression and movement — are increasingly unreliable, because modern face swaps have eliminated most visible glitches. Reliable identification requires algorithmic analysis of the video itself: generation artifacts, face-boundary inconsistencies, and signals invisible to a participant watching a compressed stream mid-conversation. Halo runs that analysis continuously on-device during the call and surfaces a risk signal when a face is synthetic, taking the judgment off the busy participant.
What is on-device deepfake detection and why does it matter for meetings?
On-device detection means the analysis runs locally on the participant's machine instead of sending call video to the cloud. For meetings this matters twice: sensitive discussions — deals, legal matters, board business — never leave the device to get a result, and detection works in real time without a network round trip. Halo runs Eva V1.6 locally, verifying faces and backgrounds continuously. ScamAI is SOC 2 Type II and GDPR compliant; with Halo, the meeting content is never shipped anywhere.
What was the $25M deepfake video call incident?
In the 2024 Arup case, a finance employee joined a video call to discuss a confidential transaction. Every other participant — including the CFO who authorized the payment — was a deepfake. Initially suspicious of an email request, the employee was reassured by familiar faces on video and transferred roughly $25M across multiple payments. It is the canonical example of why video now needs verification: being on the call is no longer proof of who is on the call.
See Halo verify a call in real time
15 minutes, on your own media. Pick a slot and leave with a result.