ZONN.ai Forensic Report

Case · 142CEE00 · 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

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 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

  • Noise Patternflagged AI · 88/100

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

  • Color Distributionread real · 18/100

    Global color distribution shape vs natural-photo baselines.

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 51
▸ expand
Noise Pattern
88
Color Distribution
18
Frequency Analysis
72
Error Level Analysis
33
Pixel Analysis
45
Compression Quality
53
Edge Consistency
49
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
400 × 400 px
Aspect
1.000
File size
32.3 KB
Bytes / pixel
0.207

Frequency Analysis

Radial1.000
DCT0.868
Upsampling1.000
Cross-channel0.023
Power-law β
-4.12
Grid energy
0.198

Edge Consistency

CV 0.518
Cell 1: 1.6148Cell 2: 1.6272Cell 3: 5.5198Cell 4: 5.5073Cell 5: 1.6137Cell 6: 2.0458Cell 7: 6.1842Cell 8: 3.6308Cell 9: 5.4769Cell 10: 3.2517Cell 11: 7.8519Cell 12: 9.3159Cell 13: 9.2855Cell 14: 5.7500Cell 15: 9.6154Cell 16: 8.4658

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

Range: 1.61379.6154

Noise Fingerprint

Variance
28.28
Std deviation
5.32
Mean
-0.0
Spatial corr.
1.126
Mean Δ
1.12
σ
1.13
CV
1.011
Uniformity
-0.011

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 10, 2026, 10:39 AM
Analyzed byAnonymous