Forensic Sensors vs AI Deepfake Detectors

SENSORS VSAI DETECTORS.

AI-only detectors learn patterns from training data and return a probability. Classical forensic sensors measure physical and structural properties directly. Sensors are easier to explain and run offline; AI models can add evidence but may fail on new generators. The strongest setup uses sensors first and optional models as extra evidence.

[ 01 ]

Explainability

A sensor result points to a measurement and a region. A learned score is harder to inspect, which matters when a finding is challenged.

[ 02 ]

Generalisation

Models can miss generators they were not trained on. Structural inconsistencies are not tied to one model.

[ 03 ]

Deployment

Deterministic sensors run offline with no model downloads. Cloud models require connectivity.

[ 04 ]

Best of both

SpearTrace's Phase 2 adapter layer allows approved models to add evidence without replacing the sensor core.

[ Questions ]

Quick answers

Are AI detectors worse?

Not always. They can add useful evidence, but they are harder to explain and depend on training data.

Why start with classical sensors?

They work offline, are explainable and are repeatable.

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