ZONN.ai Forensic Report
Case · EF231624 · IMAGE
Probably real
More signals lean toward real than AI, but some give weaker readings. Worth a second look on close inspection.
Analysed Specimen
Heads up — 2 things to know
Why this analysis might be off
We highlight every disagreement and unusual signal we found so you can judge for yourself. Stronger warnings come first; informational notes are at the bottom.
Upsampling artifacts in the frequency domain
AI evidenceFFT analysis found strong upsampling patterns — a fingerprint of diffusion-model VAE decoders (latent → pixel-space upscale).
ML detectors see this image differently
NoteML scores span a wide range (0–99). Different architectures read different feature spaces; the majority vote strengthens the consensus, but no single model is fully reliable here.
Origin Check
Trace this image elsewhere
Cross-reference the source against major reverse-image services. Each link opens in a new tab with the image URL preloaded — ZONN.ai does not re-upload the image.
Google Lens
Yandex
TinEye
Bing VisualWhy this verdict
- CommFor (4803 Generators)read real · 0/100
CommFor detector trained across 4,803 generator variants for broad coverage.
- SigLIP AI Detectorflagged AI · 99/100
SigLIP visual-language model probing semantic vs perceptual coherence.
Model Agreement
Variance across 6 ML detectors. Higher agreement means the models converged on the same reading; lower agreement means treat the verdict with care.
Evidence — 16 detectors reviewed
What each detector saw
Each detector independently gave this imagea score from 0 (definitely real) to 100 (definitely AI). The score above is their weighted consensus — detectors with higher confidence count more. No single detector decides; you read the spread.
ML Models6 detectors · mean 28▸ expand▾ collapse
Pixel & Frequency Forensics7 detectors · mean 38▸ expand▾ collapse
Provenance & Metadata3 detectors · mean 54▸ expand▾ collapse
Image Quality
- Dimensions
- 800 × 600 px
- Aspect
- 1.333
- File size
- 95.8 KB
- Bytes / pixel
- 0.204
Frequency Analysis
Edge Consistency
Per-region edge density (4 × 4 grid). Uneven distribution may indicate localized editing or splicing; uniform fields are typical of fully synthetic outputs.
Range: 4.6757 – 68.2471
Noise Fingerprint
- Variance
- 273.12
- Std deviation
- 16.53
- Mean
- -0.0
- Spatial corr.
- 7.324
- Mean Δ
- 2.23
- σ
- 2.18
- CV
- 0.979
- Uniformity
- 0.021