ZONN.ai Forensic Report

Case · 6835A46F · IMAGE

Analyzed byAnonymous
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 · 51/100

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.

AI generator fingerprints detected

AI evidence

Frequency analysis (FFT score 70/100) 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).

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

65%

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 13
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
3
SigLIP AI Detector
10
Bombek1 SigLIP+DINOv2
12
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 45
▸ expand
Color Distribution
14
Error Level Analysis
30
Noise Pattern
70
Frequency Analysis
70
Compression Quality
40
Pixel Analysis
45
Edge Consistency
49
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
800 × 600 px
Aspect
1.333
File size
53.6 KB
Bytes / pixel
0.114

Frequency Analysis

Radial1.000
DCT0.760
Upsampling1.000
Cross-channel0.022
Power-law β
-3.39
Grid energy
0.360

Edge Consistency

CV 0.543
Cell 1: 4.1938Cell 2: 16.6325Cell 3: 5.2097Cell 4: 7.9515Cell 5: 5.3710Cell 6: 24.1956Cell 7: 6.4531Cell 8: 8.0084Cell 9: 8.0868Cell 10: 18.5932Cell 11: 14.4196Cell 12: 22.0981Cell 13: 6.4014Cell 14: 9.2505Cell 15: 14.5239Cell 16: 8.8139

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

Range: 4.193824.1956

Noise Fingerprint

Variance
72.88
Std deviation
8.54
Mean
-0.0
Spatial corr.
2.734
Mean Δ
1.42
σ
1.48
CV
1.043
Uniformity
-0.043

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 14, 2026, 6:19 PM
Analyzed byAnonymous