Dual-Use Deeptech · Cybersecurity & National Defence

Defeating deepfakes with physics — not more AI

UnCognito is a dual-use deeptech company applying physics and acoustics to solve critical problems in cybersecurity and national defence — particularly at the vulnerable edge. Built on deep R&D expertise spanning naval acoustics, oil exploration, high-performance computing and AI.

What we build

Two product pillars. One fundamental edge.

UnCognito is a dual-use deeptech company applying physics and acoustics to solve critical problems in cybersecurity and national defence — particularly at the vulnerable edge. Built on deep R&D expertise spanning naval acoustics, oil exploration, high-performance computing and AI.

Cybersecurity / Deepfake Detection

Real-time, high-throughput detection of synthetic media across audio, video, and visual channels. Built for enterprise security stacks and government environments.

  • Audio deepfake detection — ~0.34% false positive rate
  • Visual deepfake detection — physics-based
  • Real-time video analysis for live streams
  • Embedded active countermeasures
  • Enterprise integration capability

Defence Acoustics

"Listen up to shoot down" — a passive acoustic detection system integrated with automated response capabilities for drone defence and battlefield awareness.

  • Detect drones via acoustic signatures
  • Minimal electronic footprint (stealth advantage)
  • Front-line, rear echelon & vehicle-mounted
  • Manoeuverist and precision warfare support
  • Automated response integration
The Approach

Why physics wins where AI falls short

AI systems chasing evolving generative models are locked in an arms race they cannot win. Physics-based detection is orthogonal to that race entirely.

01. Structural, not model-dependent

Physics detects inconsistencies that synthetic media cannot conceal, regardless of how the model evolves.

02. Lower latency and compute cost

No need for large AI inference pipelines — physics-based checks run efficiently in real time.

03. Stable under adversarial iteration

As attackers iterate on generative models, our detection does not degrade — it remains anchored in physical reality.

04. Built for demanding environments

Deployable offline, at the edge, in classified environments — where heavyweight cloud AI cannot go.

ATTRIBUTE AI vs AI PHYSICS-BASED
Approach Arms race dynamics Orthogonal
Compute High Low
Detection Method Reactive / model-based Structural
Over Time Degrades Stable
Edge / Offline Limited Native
Latency High Real-time
© We4C.ai LLC, DBA UnCognito