ZONN.ai Forensic Report

Case · 29844C69 · 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 · 23/100

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

4 ML detectors did not respond (Ml Python Siglip, Ml Commfor, Itsnotai V2, Xrayon Convnext). 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 23/100. 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 · 21/100

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

Evidence — 15 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 Models5 detectors · mean 50
▸ expand
SigLIP AI Detector
50
CommFor (4803 Generators)
50
INA v2 (FLUX/MJ)
50
xRayon ConvNeXtV2
50
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 46
▸ expand
Error Level Analysis
21
Frequency Analysis
73
Pixel Analysis
35
Noise Pattern
64
Color Distribution
39
Edge Consistency
43
Compression Quality
45
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1086 × 1448 px
Aspect
0.750
File size
193.8 KB
Bytes / pixel
0.126

Frequency Analysis

Radial1.000
DCT0.835
Upsampling1.000
Cross-channel0.092
Power-law β
-3.38
Grid energy
0.248

Edge Consistency

CV 0.698
Cell 1: 8.2712Cell 2: 11.1964Cell 3: 12.8364Cell 4: 5.8010Cell 5: 10.1334Cell 6: 36.3768Cell 7: 20.4562Cell 8: 8.1859Cell 9: 4.9537Cell 10: 16.5975Cell 11: 16.4471Cell 12: 2.5933Cell 13: 6.1492Cell 14: 11.0004Cell 15: 28.5253Cell 16: 4.6805

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

Range: 2.593336.3768

Noise Fingerprint

Variance
92.29
Std deviation
9.61
Mean
-0.0
Spatial corr.
2.986
Mean Δ
1.88
σ
2.24
CV
1.191
Uniformity
-0.191

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 13, 2026, 2:20 AM
Analyzed byAnonymous