Metadata and provenance
Capture devices, software tags and source pages can support or weaken authenticity hypotheses.
Guide
AI image detection is strongest when model signals and source evidence are reviewed together.
Synthetic-image analysis should combine metadata, compression, visual artifacts, source context and confidence scoring instead of relying on a single yes/no claim.
Designed for analysts who need clear signals, not scattered tabs.
Visual and compression clues
Artifacts around hands, text, reflections, repeated patterns and compression can be useful, but they are not proof alone.
Responsible reporting
AI detection should avoid overclaiming. The best output explains evidence, confidence and what remains unknown.
Common questions
Can AI image detection be 100% certain?
No. Synthetic-image detection is probabilistic and should be combined with provenance and source checks.
Why use image authenticity and AI detection together?
Authenticity work considers file history and source context, while AI detection focuses on synthetic-generation indicators.
Start investigation
Use OsintNET to convert public signals into structured evidence.
Pick the module that matches your target and keep each clue connected to its source, confidence and investigation context.