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On-device deepfake detection agent
Real-time detection that runs on the device — media and identity verified locally, with nothing leaving the endpoint. Shipping today in Halo; an embeddable agent for your own apps and devices is in development.
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a deepfake fraud attempt occurred every five minutes in 2024Source: Entrust Identity Fraud Report
- Runs on-device, private by default
- Real-time verification at the edge
- Live today in Halo; embeddable agent in development
Detection at the edge
The same Eva V1.6 detection runs locally on the endpoint: deepfakes, forged documents, and manipulated media flagged in real time without a round trip to the cloud. Halo ships this today for live video meetings. Packaging the same engine as an embeddable agent your own app or device can call is in development, alongside the native SDKs on the API roadmap.
Private and always on
Because analysis happens on the endpoint, sensitive media never leaves the device — ideal for regulated environments, offline scenarios, and low-latency flows where cloud calls aren't an option.
Data minimization as a compliance position
To check media for manipulation, most architectures ship it to someone's cloud — and for regulated deployments that round trip is itself a compliance event, raising GDPR questions about data minimization, purpose limitation, and cross-border transfer. On-device detection removes the event entirely: Eva V1.6 runs locally and only the result leaves the device, so a privacy impact assessment has no new data flow to assess. ScamAI is SOC 2 Type II and GDPR compliant, and your users' faces never become someone else's dataset.
Where an edge detection agent fits
The embeddable agent is being built for capture-time checks inside banking and fintech apps, kiosks and retail counters that need to verify the person standing there, and field or regulated endpoints where data cannot leave the machine. Today the shipping path for on-device detection is Halo on the participant's own machine, with the REST API covering every other integration in the meantime. If one of these patterns is yours, tell us — early access shapes what we build first.
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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.
- Compliance posture: explainable, auditable results give regulators and auditors evidence, not a black-box score.
Common questions
What is on-device deepfake detection?
On-device deepfake detection runs the model on the endpoint — a phone, kiosk, laptop, or embedded device — instead of sending media to a cloud API. The device captures a face image, document photo, or video frame; the local model scores it for AI generation and manipulation; the app gets a result in real time. Because analysis happens where media is captured, nothing sensitive leaves the device: lower latency, offline operation, no new data flow for privacy review.
Does the on-device agent work without an internet connection?
Yes — that is the point of running inference on the endpoint: results do not depend on connectivity. Halo already works this way, analyzing meeting video locally on the participant's machine. The same property is what makes the embeddable agent, currently in development, suited to kiosks in low-coverage locations, field workflows like claims adjusting or benefits processing in remote areas, and any environment where the network is untrusted or intermittent. Model updates arrive when the device connects, and result metadata can queue for later sync if you want centralized reporting.
How is an on-device agent different from calling a detection API?
Both return the same thing: a manipulation result with signals from the Eva V1.6 model. The difference is where the media goes. With the REST API — available today, in any language — your backend sends media to ScamAI, which analyzes, scores, and discards it; right for server-side pipelines like upload moderation or claims processing. On-device, the model runs on the endpoint instead, so media never leaves it: today that ships as Halo for live meetings, and as an embeddable agent for your own apps once it is out of development. Teams choose on-device for privacy-sensitive capture, offline operation, and latency budgets.
See ScamAI on your use case
15 minutes, on your own media. Pick a slot and leave with a result.