ZONN.ai Forensic Report

Case · EF02DF4A · 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 — 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

  • Error Level Analysisread real · 10/100

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

  • Noise Patternflagged AI · 83/100

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

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 47
▸ expand
Error Level Analysis
10
Noise Pattern
83
Color Distribution
21
Frequency Analysis
72
Edge Consistency
35
Pixel Analysis
57
Compression Quality
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
71.1 KB
Bytes / pixel
0.069

Frequency Analysis

Radial1.000
DCT0.817
Upsampling1.000
Cross-channel0.070
Power-law β
-3.25
Grid energy
0.275

Edge Consistency

CV 0.841
Cell 1: 1.9455Cell 2: 18.7539Cell 3: 4.1629Cell 4: 1.2172Cell 5: 3.2692Cell 6: 19.2037Cell 7: 7.1075Cell 8: 1.8389Cell 9: 1.9046Cell 10: 8.1656Cell 11: 5.7725Cell 12: 3.7304Cell 13: 3.8722Cell 14: 10.9895Cell 15: 10.0576Cell 16: 2.6859

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

Range: 1.217219.2037

Noise Fingerprint

Variance
41.97
Std deviation
6.48
Mean
0.0
Spatial corr.
1.642
Mean Δ
1.14
σ
1.53
CV
1.343
Uniformity
-0.343

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 12, 2026, 12:32 PM
Analyzed byAnonymous