ZONN.ai Forensic Report

Case · 024BAA89 · IMAGE

ZAnalyzed by@zonner
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 (4–100). 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)flagged AI · 100/100

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

  • xRayon ConvNeXtV2read real · 4/100

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

Model Agreement

34%

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
INA v2 (FLUX/MJ)
100
xRayon ConvNeXtV2
4
Bombek1 SigLIP+DINOv2
4
SigLIP AI Detector
25
CommFor (4803 Generators)
42
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 50
▸ expand
Noise Pattern
88
Error Level Analysis
23
Frequency Analysis
73
Color Distribution
33
Pixel Analysis
35
Edge Consistency
48
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
454 × 613 px
Aspect
0.741
File size
363.1 KB
Bytes / pixel
1.336

Frequency Analysis

Radial1.000
DCT0.884
Upsampling1.000
Cross-channel0.017
Power-law β
-4.06
Grid energy
0.174

Edge Consistency

CV 0.557
Cell 1: 2.6072Cell 2: 4.3152Cell 3: 3.8947Cell 4: 1.3177Cell 5: 2.3960Cell 6: 4.8154Cell 7: 4.4166Cell 8: 1.3879Cell 9: 6.5580Cell 10: 10.7777Cell 11: 6.6489Cell 12: 3.4147Cell 13: 6.1608Cell 14: 8.2158Cell 15: 4.8276Cell 16: 11.3890

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

Range: 1.317711.3890

Noise Fingerprint

Variance
29.51
Std deviation
5.43
Mean
-0.0
Spatial corr.
1.052
Mean Δ
1.17
σ
1.36
CV
1.159
Uniformity
-0.159

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 16, 2026, 10:35 AM
Analyzed by@zonner