ZONN.ai Forensic Report

Case · 19C8688F · IMAGE

WAnalyzed by@walena
ZONN Analysis
0

Very likely real

Most signals point to a real, human-captured source. Detection tools are not perfect — treat this as a strong indication, not a verdict.

Signal ConfidenceLimited · 42/100

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 67/100) 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).

Uneven compression (ELA)

AI evidence

ELA coefficient of variation is 1.76 — different regions show noticeably different compression levels. Common with composites, edits, or AI generations.

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

  • INA v2 (FLUX/MJ)read real · 0/100

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

  • Error Level Analysisread real · 0/100

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

Model Agreement

64%

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 25
▸ expand
INA v2 (FLUX/MJ)
0
Bombek1 SigLIP+DINOv2
10
xRayon ConvNeXtV2
12
CommFor (4803 Generators)
33
SigLIP AI Detector
42
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 38
▸ expand
Error Level Analysis
0
Pixel Analysis
17
Color Distribution
28
Frequency Analysis
67
Noise Pattern
61
Edge Consistency
46
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1389 × 763 px
Aspect
1.820
File size
1.00 MB
Bytes / pixel
0.993

Frequency Analysis

Radial0.850
DCT0.768
Upsampling1.000
Cross-channel0.105
Power-law β
-2.60
Grid energy
0.348

Edge Consistency

CV 0.626
Cell 1: 13.1055Cell 2: 5.8693Cell 3: 13.2249Cell 4: 16.3098Cell 5: 8.1327Cell 6: 6.5356Cell 7: 1.9079Cell 8: 2.9506Cell 9: 14.7247Cell 10: 4.5913Cell 11: 2.5078Cell 12: 13.7145Cell 13: 3.4052Cell 14: 3.3953Cell 15: 3.5098Cell 16: 11.2357

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

Range: 1.907916.3098

Noise Fingerprint

Variance
123.09
Std deviation
11.09
Mean
-0.3
Spatial corr.
1.991
Mean Δ
1.40
σ
2.47
CV
1.759
Uniformity
-0.759

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 15, 2026, 8:49 PM
Analyzed by@walena