ZONN.ai Forensic Report

Case · 50B8A0F6 · 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 · 28/100

Analysed Specimen

Original analysed image
Forensic suspicion heatmap
OriginalHeatmap
POS55/100
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 28/100. 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

  • Error Level Analysisread real · 26/100

    Error Level Analysis. Re-saves and diffs to expose uneven compression regions.

  • Frequency Analysisflagged AI · 73/100

    Fast-Fourier inspection. Diffusion outputs leave periodic spectral peaks.

Model Agreement

100%

Variance across 3 ML detectors. Higher agreement means the models converged on the same reading; lower agreement means treat the verdict with care.

Evidence — 13 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 Models3 detectors · mean 50
▸ expand
SigLIP AI Detector
50
CommFor (4803 Generators)
50
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Error Level Analysis
26
Frequency Analysis
73
Noise Pattern
68
Edge Consistency
35
Color Distribution
37
Pixel Analysis
57
Compression Quality
45
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1105 × 1423 px
Aspect
0.777
File size
195.4 KB
Bytes / pixel
0.127

Frequency Analysis

Radial1.000
DCT0.852
Upsampling1.000
Cross-channel0.075
Power-law β
-3.31
Grid energy
0.222

Edge Consistency

CV 0.808
Cell 1: 3.7332Cell 2: 7.9982Cell 3: 21.6960Cell 4: 3.0288Cell 5: 4.4754Cell 6: 11.6276Cell 7: 26.0466Cell 8: 4.4457Cell 9: 2.7829Cell 10: 12.1448Cell 11: 29.4352Cell 12: 4.4612Cell 13: 4.3310Cell 14: 6.8107Cell 15: 27.5321Cell 16: 10.4430

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

Range: 2.782929.4352

Noise Fingerprint

Variance
85.15
Std deviation
9.23
Mean
0.0
Spatial corr.
2.681
Mean Δ
1.70
σ
1.89
CV
1.113
Uniformity
-0.113

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 12, 2026, 8:38 PM
Analyzed byAnonymous