ZONN.ai Forensic Report

Case · 2BA58FB9 · 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 · 29/100

Analysed Specimen

Original analysed image
Forensic suspicion heatmap
OriginalHeatmap
POS55/100
No flagged regions

Heads up — 4 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).

Uneven compression (ELA)

AI evidence

ELA coefficient of variation is 3.71 — different regions show noticeably different compression levels. Common with composites, edits, or AI generations.

Weak overall confidence

Note

Aggregate verdict confidence is 29/100. 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 · 0/100

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

  • Pixel Analysisread real · 17/100

    Low-level pixel statistics: saturation cliffs, channel histograms, banding.

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 43
▸ expand
Error Level Analysis
0
Pixel Analysis
17
Noise Pattern
80
Frequency Analysis
72
Edge Consistency
35
Color Distribution
40
Compression Quality
55
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1254 × 1254 px
Aspect
1.000
File size
681.6 KB
Bytes / pixel
0.444

Frequency Analysis

Radial1.000
DCT0.834
Upsampling1.000
Cross-channel0.047
Power-law β
-3.49
Grid energy
0.249

Edge Consistency

CV 1.109
Cell 1: 0.0000Cell 2: 2.7465Cell 3: 1.5767Cell 4: 0.0000Cell 5: 1.6125Cell 6: 9.2231Cell 7: 10.5377Cell 8: 3.0634Cell 9: 2.6177Cell 10: 10.8273Cell 11: 10.8086Cell 12: 3.1572Cell 13: 0.0000Cell 14: 0.9632Cell 15: 0.9894Cell 16: 0.0000

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

Range: 0.000010.8273

Noise Fingerprint

Variance
63.39
Std deviation
7.96
Mean
-0.2
Spatial corr.
0.988
Mean Δ
0.35
σ
1.29
CV
3.713
Uniformity
-2.713

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 12, 2026, 7:07 AM
Analyzed byAnonymous