Spot deepfakes in seconds.
Upload a face video and run a protected forensic analysis across visual, motion, and consistency signals with supporting evidence.
Output
Verdict + evidence map
Confidence, consistency checks, face-detection quality, and exportable evidence.
Multi-signal forensic review
Visual integrity
Looks for subtle texture, edge, and facial-detail inconsistencies that may indicate manipulation.
Motion consistency
Checks whether facial movement and frame-to-frame transitions remain natural throughout the clip.
Identity coherence
Reviews broader facial consistency so the final assessment is based on more than a single frame.
The workflow checks multiple complementary evidence signals before showing a verdict, so a result is not based on a single visual cue.
Try a pre-loaded example
Preview the video, then run the analysisFF++ C23
FaceForensics++ original
Untouched original from the FF++ in-distribution test split.
FF++ C23
FaceForensics++ Deepfake
FaceSwap/Deepfakes manipulation. The model usually nails this with > 0.99 confidence.
Celeb-DF v2
Celeb-DF v2 real
Authentic celebrity footage held out of training entirely.
Celeb-DF v2
Celeb-DF v2 synthesis
Cross-dataset deepfake — harder than FF++ since the manipulation pipeline differs from training.
Or upload your own video
Checking APIDrop a video here
or click to browse · max 50 MB · .mp4 / .mov / .m4v
A preview appears before the file is sent to the GPU backend.