Misinformation detection

Fact-checkers combine traditional verification — sourcing the original upload, checking location and timing, contacting the people depicted — with forensic analysis of the media, examining faces and frames for generation artifacts and manipulation traces that survive compression. The forensic layer matters most when a fake is well made and context checks are inconclusive. ScamAI provides that layer through the REST API: a scored result with the signals that fired, delivered fast enough to inform coverage before the content peaks.

The liar's dividend is the benefit bad actors gain from the existence of deepfakes: because the public knows video can be fabricated, genuine footage can be dismissed as fake. The term describes a second-order harm of synthetic media — beyond deceiving people with fakes, it corrodes the evidentiary value of everything real. Countering it requires the ability to affirm authenticity, not just flag manipulation — so results that clear genuine media matter as much as results that catch fakes.

Detection models score media in both directions: a result can flag signs of AI generation or manipulation, or report none are present, with evidence behind either conclusion. A clean forensic result is not absolute proof, but combined with provenance and sourcing it gives newsrooms and platforms grounds to treat footage as genuine — and resist pressure to retract real material dismissed as fake. ScamAI attaches the fired signals to every result so the reasoning is citable, not just the score.