How PhotoProof Labs Combines Multiple Signals Into One Decision

Why one signal is not enough

A single AI-generation classifier can be confidently wrong: compression can mimic generation artifacts, and increasingly capable generators can pass visual-pattern checks that older detectors relied on. Treating one model's output as a verdict inherits every failure mode of that one model.

The five layers, briefly

Each layer answers a narrower, more falsifiable question than 'is this AI?' on its own.

• Visual AI pattern analysis — statistical and textural artifacts characteristic of generative models
• EXIF and metadata review — presence, consistency, and plausibility of camera/software metadata
• Compression and recapture signals — double-compression and re-encoding traces
• Manipulation and retouching traces — localized edit and splicing indicators
• Semantic scene plausibility — physical and contextual consistency of the depicted scene

How the layers are combined

Layers are not averaged into a single number blindly. Agreement across independent layers raises confidence; when layers disagree, the system reports that disagreement through a lower confidence score rather than silently picking a winner. This is why PhotoProof Labs reports probability and confidence as two separate numbers — see the confidence scoring page for the full explanation.

What this does and doesn't solve

Multi-signal combination reduces — it does not eliminate — the risk of a single misleading signal driving the result. An image that defeats all five layers simultaneously is possible, especially for novel generators not yet reflected in the visual pattern layer. This is why PhotoProof Labs reports probabilistic evidence, not a certified verdict.

Which signal is weighted the most?

None is fixed as dominant. Weighting depends on which signals are actually available and reliable for a given file — for example, a screenshot with no EXIF data leans more heavily on visual and semantic signals by necessity.

Can a user see which signals disagreed?

The analysis report breaks out each signal individually, alongside the combined probability and confidence score, specifically so a disagreement between layers is visible rather than hidden inside one number.