Manual-Edit Forensics — image forgery detection

Yes — through forensic signals rather than generative-AI fingerprints. Manual edits disturb an image's internal statistics: spliced content imports mismatched noise and compression history, cloned patches repeat pixels improbably, and retouching breaks the uniform sensor noise a real camera produces. ScamAI's forensics reads these traces on selfies and documents, returns a confidence score with the signals that fired, and localizes the edited region — the flag points at the altered field, not vaguely at the whole image.

Localization means identifying where in an image or document the manipulation occurred, not merely that it occurred somewhere. Instead of a file-level fake/real label, the result highlights the specific region whose forensic statistics are inconsistent — a pasted date field, a cloned background patch, a retouched face area. That precision changes operations: reviewers confirm or clear a flag in seconds, disputes can be answered with concrete evidence, and audit trails record exactly what was found rather than an opaque score.

Because they catch different artifact classes. Deepfake detectors hunt the statistical fingerprints of generative models — and a hand-made edit contains none of them. A document field altered in an image editor can sail past a purely generative detector while remaining fully fraudulent. Attackers use whichever tool is cheapest for the job, so coverage needs both: ScamAI runs generative detection and manual-edit forensics in the same pass, giving a hand-made fake the same scrutiny as a synthetic one.