ZONN.ai Forensic Report

Case · 90161EB3 · 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 · 46/100

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

  • INA v2 (FLUX/MJ)read real · 0/100

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

  • xRayon ConvNeXtV2flagged AI · 95/100

    ConvNeXtV2 detector trained on FLUX, DALL-E 3, SDXL, SD3.5, and Midjourney v6.

Model Agreement

37%

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 35
▸ expand
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
95
Bombek1 SigLIP+DINOv2
6
SigLIP AI Detector
26
CommFor (4803 Generators)
35
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 54
▸ expand
Noise Pattern
80
Pixel Analysis
75
Frequency Analysis
74
Color Distribution
28
Error Level Analysis
30
Compression Quality
45
Edge Consistency
47
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
987 × 1099 px
Aspect
0.898
File size
121.5 KB
Bytes / pixel
0.115

Frequency Analysis

Radial1.000
DCT0.814
Upsampling1.000
Cross-channel0.145
Power-law β
-3.47
Grid energy
0.279

Edge Consistency

CV 0.598
Cell 1: 3.3120Cell 2: 5.4343Cell 3: 5.7842Cell 4: 4.1940Cell 5: 6.7525Cell 6: 8.9312Cell 7: 10.7959Cell 8: 3.3961Cell 9: 8.3576Cell 10: 18.3074Cell 11: 19.3025Cell 12: 6.8740Cell 13: 3.9768Cell 14: 15.3272Cell 15: 18.0531Cell 16: 6.3279

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

Range: 3.312019.3025

Noise Fingerprint

Variance
45.19
Std deviation
6.72
Mean
-0.0
Spatial corr.
2.139
Mean Δ
1.67
σ
1.75
CV
1.047
Uniformity
-0.047

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 16, 2026, 7:42 PM
Analyzed byAnonymous