ZONN.ai Forensic Report

Case · BB56528F · 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 · 48/100

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 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).

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 ConvNeXtV2read real · 4/100

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

Model Agreement

36%

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 33
▸ expand
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
4
SigLIP AI Detector
95
CommFor (4803 Generators)
20
Bombek1 SigLIP+DINOv2
31
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Noise Pattern
82
Error Level Analysis
23
Frequency Analysis
70
Color Distribution
35
Edge Consistency
41
Pixel Analysis
45
Compression Quality
45
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1500 × 2000 px
Aspect
0.750
File size
337.0 KB
Bytes / pixel
0.115

Frequency Analysis

Radial1.000
DCT0.790
Upsampling1.000
Cross-channel0.023
Power-law β
-3.37
Grid energy
0.315

Edge Consistency

CV 0.782
Cell 1: 10.7742Cell 2: 11.8214Cell 3: 3.7631Cell 4: 9.4441Cell 5: 21.6674Cell 6: 21.3615Cell 7: 9.9202Cell 8: 5.7200Cell 9: 19.3104Cell 10: 13.4075Cell 11: 3.5582Cell 12: 1.4609Cell 13: 3.9168Cell 14: 2.1113Cell 15: 1.3992Cell 16: 1.5416

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

Range: 1.399221.6674

Noise Fingerprint

Variance
37.68
Std deviation
6.14
Mean
-0.0
Spatial corr.
2.137
Mean Δ
1.25
σ
1.44
CV
1.150
Uniformity
-0.150

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 16, 2026, 9:48 AM
Analyzed by@zonner