ZONN.ai Forensic Report

Case · 03A612B8 · 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 ConfidenceLimited · 44/100

Analysed Specimen

Original analysed image
Forensic suspicion heatmap
OriginalHeatmap
POS55/100
No flagged regions

Heads up — 2 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.

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).

ML detectors see this image differently

Note

ML scores span a wide range (0–95). Different architectures read different feature spaces; the majority vote strengthens the consensus, but no single model is fully reliable here.

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

  • CommFor (4803 Generators)read real · 0/100

    CommFor detector trained across 4,803 generator variants for broad coverage.

  • INA v2 (FLUX/MJ)read real · 1/100

    BEiT-Large dual-head classifier trained on FLUX, Midjourney, and real photo corpora.

Model Agreement

35%

Variance across 6 ML detectors. Higher agreement means the models converged on the same reading; lower agreement means treat the verdict with care.

Evidence — 16 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 Models6 detectors · mean 38
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
1
xRayon ConvNeXtV2
95
Bombek1 SigLIP+DINOv2
35
SigLIP AI Detector
46
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 48
▸ expand
Color Distribution
19
Noise Pattern
80
Error Level Analysis
30
Frequency Analysis
70
Edge Consistency
44
Pixel Analysis
45
Compression Quality
45
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
900 × 1200 px
Aspect
0.750
File size
60.8 KB
Bytes / pixel
0.058

Frequency Analysis

Radial1.000
DCT0.762
Upsampling1.000
Cross-channel0.027
Power-law β
-2.94
Grid energy
0.356

Edge Consistency

CV 0.669
Cell 1: 2.8719Cell 2: 1.0247Cell 3: 1.2955Cell 4: 0.7219Cell 5: 11.4041Cell 6: 6.3871Cell 7: 7.9986Cell 8: 13.9686Cell 9: 17.3417Cell 10: 17.5202Cell 11: 8.1948Cell 12: 9.1329Cell 13: 6.5704Cell 14: 18.6347Cell 15: 19.3655Cell 16: 6.5179

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

Range: 0.721919.3655

Noise Fingerprint

Variance
45.65
Std deviation
6.76
Mean
-0.0
Spatial corr.
2.140
Mean Δ
1.62
σ
1.69
CV
1.046
Uniformity
-0.046

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 15, 2026, 10:57 AM
Analyzed byAnonymous