Everyone wants a deepfake detector app on their phone. The suspicious stuff arrives in chat threads and social feeds, not on a desktop. I get it. But the app stores are a minefield of scanners that overpromise, so let us think about the ones worth comparing.
First, forget definitive rankings. This space moves too fast, and most app lists are SEO bait with affiliate links. I do not trust them and neither should you. What matters is matching an app to your actual need.
Start with modality. Some apps check images, some do video, almost none do audio well. A deepfake detector app that only handles stills is useless against a suspicious video. Check the modality before the star rating. Always.
Then ask where the analysis happens. On-device keeps your media private but fights phone hardware limits. Cloud can run bigger models but uploads your files to someone's server. For sensitive media, that distinction is the entire decision.
Look at what the app actually tells you. A bare real-or-fake verdict with a dramatic percentage is confidence theater. Better apps explain their reasoning, highlight suspicious regions, or at least publish something about their method. If an app cannot say how it decides, it has not decided anything worth trusting.
The privacy policy matters more than the feature list. Read what happens to your uploads. Retention periods. Third-party sharing. Whether your face trains their next model. Boring reading. Important answers.
And keep expectations honest, because the stores will not. No mobile app catches everything, and the good ones admit it. A deepfake scanner app for iOS or Android is triage. A first opinion before you escalate. Any app promising certainty is a red flag in itself.
Shop by comparing candidates on the same criteria: modality coverage, on-device versus cloud, explanation quality, privacy terms, price. Whether you need a mobile app to detect deepfake video or just an AI generated image checker app free tier for casual use, the criteria do not change. The database at deepfakedetect.fyi tracks detection tools with claimed capabilities and limitations, which is a more grounded starting point than app-store roulette.