ZONN.ai Forensic Report

Case · 4CBBBE4A · 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

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 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

  • Color Distributionread real · 13/100

    Global color distribution shape vs natural-photo baselines.

  • Noise Patternflagged AI · 81/100

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

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 48
▸ expand
Color Distribution
13
Noise Pattern
81
Error Level Analysis
23
Frequency Analysis
76
Pixel Analysis
35
Compression Quality
56
Edge Consistency
53
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

AI-typical dimensions
Dimensions
1024 × 1024 px
Aspect
1.000
File size
277.3 KB
Bytes / pixel
0.271

Frequency Analysis

Radial1.000
DCT0.860
Upsampling1.000
Cross-channel0.176
Power-law β
-3.60
Grid energy
0.211

Edge Consistency

CV 0.449
Cell 1: 7.7022Cell 2: 7.2115Cell 3: 6.8304Cell 4: 5.0792Cell 5: 9.1612Cell 6: 3.6010Cell 7: 3.0976Cell 8: 4.8638Cell 9: 11.7215Cell 10: 2.3189Cell 11: 2.5234Cell 12: 10.8911Cell 13: 8.9342Cell 14: 10.4583Cell 15: 12.2990Cell 16: 10.0606

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

Range: 2.318912.2990

Noise Fingerprint

Variance
46.83
Std deviation
6.84
Mean
0.0
Spatial corr.
1.705
Mean Δ
1.77
σ
2.05
CV
1.159
Uniformity
-0.159

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 10, 2026, 10:41 AM
Analyzed byAnonymous