ZONN.ai Forensic Report

Case · 31C9D437 · IMAGE

Analyzed byAnonymous
ZONN Analysis
0

Probably real

More signals lean toward real than AI, but some give weaker readings. Worth a second look on close inspection.

Signal ConfidenceWeak · 30/100

Analysed Specimen

Analysed content
No flagged regions

Heads up — 3 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.

Multiple detectors unreachable

Note

2 ML detectors did not respond (Ml Python Siglip, Ml Commfor). The verdict was computed with reduced evidence; reliability is lower than usual.

Upsampling artifacts in the frequency domain

AI evidence

FFT analysis found strong upsampling patterns — a fingerprint of diffusion-model VAE decoders (latent → pixel-space upscale).

Weak overall confidence

Note

Aggregate verdict confidence is 30/100. Several detectors returned uncertain answers or were offline. Read the verdict as a guideline, not as a final answer.

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.

Why this verdict

  • Noise Patternflagged AI · 97/100

    Sensor-noise (PRNU) fingerprint check. Real cameras leave camera-unique noise.

  • Color Distributionread real · 19/100

    Global color distribution shape vs natural-photo baselines.

Model Agreement

100%

Variance across 2 ML detectors. Higher agreement means the models converged on the same reading; lower agreement means treat the verdict with care.

Evidence — 12 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 Models2 detectors · mean 50
▸ expand
SigLIP AI Detector
50
CommFor (4803 Generators)
50
Pixel & Frequency Forensics7 detectors · mean 54
▸ expand
Noise Pattern
97
Color Distribution
19
Error Level Analysis
26
Frequency Analysis
74
Pixel Analysis
57
Edge Consistency
53
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
470 × 616 px
Aspect
0.763
File size
250.5 KB
Bytes / pixel
0.886

Frequency Analysis

Radial1.000
DCT0.918
Upsampling1.000
Cross-channel0.022
Power-law β
-4.66
Grid energy
0.123

Edge Consistency

CV 0.434
Cell 1: 1.7972Cell 2: 1.8560Cell 3: 1.8571Cell 4: 2.3493Cell 5: 2.0527Cell 6: 1.7157Cell 7: 2.2175Cell 8: 5.1822Cell 9: 1.1093Cell 10: 1.5285Cell 11: 1.2479Cell 12: 1.8271Cell 13: 3.4885Cell 14: 1.9997Cell 15: 2.2932Cell 16: 1.9353

Per-region edge density (4 × 4 grid). Uneven distribution may indicate localized editing or splicing; uniform fields are typical of fully synthetic outputs.

Range: 1.10935.1822

Noise Fingerprint

Variance
4.46
Std deviation
2.11
Mean
-0.0
Spatial corr.
0.424
Mean Δ
0.81
σ
0.90
CV
1.108
Uniformity
-0.108

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 10, 2026, 10:40 AM
Analyzed byAnonymous