Photo forensics
See the evidence behind the result.
AI Screener turns classic and learned image-forensics methods into visual explanations. Repeated patterns, recompression, noise changes, seams, and metadata are interpreted as clues—not proof.
Copy-move matching
Repeated local keypoints can reveal duplicated content while explicitly accounting for patterned backgrounds.
Noise and seam maps
Regional sensor/compression texture and local edge energy help locate abrupt processing changes.
Frequency and recompression
FFT, DCT, ELA, double-quantization, and recompression sweeps describe editing and recapture clues.
Learned localization
Open forgery-localization models provide additional heatmaps that are compared with classic signals.
No automated detector can prove authenticity. AI Screener combines independent signals, shows the evidence, and leaves the decision with a human.
Run the free checkerQuestions to ask of the result
Limits matter as much as the signals.
Does editing metadata make an image fake?
No. Missing or changed metadata is contextual evidence only; social platforms and normal editors often remove it.