ZONN.ai Forensic Report

Case · B1453F18 · IMAGE

AAnalyzed by@aivampire
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 · 94/100

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

  • Frequency Analysisflagged AI · 73/100

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

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 55
▸ expand
Noise Pattern
94
Frequency Analysis
73
Color Distribution
32
Error Level Analysis
34
Pixel Analysis
45
Edge Consistency
54
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
800 × 800 px
Aspect
1.000
File size
69.4 KB
Bytes / pixel
0.111

Frequency Analysis

Radial1.000
DCT0.859
Upsampling1.000
Cross-channel0.052
Power-law β
-4.08
Grid energy
0.212

Edge Consistency

CV 0.427
Cell 1: 2.3304Cell 2: 3.6249Cell 3: 4.9937Cell 4: 1.5897Cell 5: 2.0406Cell 6: 2.7862Cell 7: 5.7516Cell 8: 2.3423Cell 9: 2.3223Cell 10: 2.5021Cell 11: 6.4877Cell 12: 4.2582Cell 13: 3.0734Cell 14: 5.2429Cell 15: 6.5502Cell 16: 3.3610

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

Range: 1.58976.5502

Noise Fingerprint

Variance
10.20
Std deviation
3.19
Mean
0.0
Spatial corr.
0.855
Mean Δ
0.99
σ
0.98
CV
0.992
Uniformity
0.008

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 10, 2026, 3:45 PM
Analyzed by@aivampire