ZONN.ai Forensic Report

Case · 32D39BC1 · 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 · 53/100

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.

AI generator fingerprints detected

AI evidence

Frequency analysis (FFT score 73/100) shows modern diffusion-style upsampling patterns, but the ML models say "real". This combination is a known blind spot for newer generators (SDXL, FLUX, Midjourney v6) — the verdict above may be misleading.

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 · 0/100

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

Model Agreement

29%

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 26
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
0
Bombek1 SigLIP+DINOv2
2
SigLIP AI Detector
95
xRayon ConvNeXtV2
9
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 50
▸ expand
Noise Pattern
80
Color Distribution
26
Frequency Analysis
73
Error Level Analysis
32
Compression Quality
40
Pixel Analysis
45
Edge Consistency
51
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
900 × 676 px
Aspect
1.331
File size
80.4 KB
Bytes / pixel
0.135

Frequency Analysis

Radial1.000
DCT0.890
Upsampling1.000
Cross-channel0.018
Power-law β
-4.41
Grid energy
0.166

Edge Consistency

CV 0.474
Cell 1: 5.1368Cell 2: 4.2614Cell 3: 5.2407Cell 4: 6.8680Cell 5: 14.1615Cell 6: 14.7459Cell 7: 13.4734Cell 8: 10.4166Cell 9: 10.4170Cell 10: 10.4592Cell 11: 8.8284Cell 12: 7.7122Cell 13: 5.5251Cell 14: 3.1581Cell 15: 3.0944Cell 16: 4.3877

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

Range: 3.094414.7459

Noise Fingerprint

Variance
49.50
Std deviation
7.04
Mean
-0.0
Spatial corr.
1.837
Mean Δ
1.27
σ
1.29
CV
1.020
Uniformity
-0.020

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 14, 2026, 9:19 AM
Analyzed byAnonymous