ZONN.ai Forensic Report

Case · EF741E33 · 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 · 49/100

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 77/100) 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 (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

32%

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 32
▸ expand
INA v2 (FLUX/MJ)
100
xRayon ConvNeXtV2
4
Bombek1 SigLIP+DINOv2
5
SigLIP AI Detector
13
CommFor (4803 Generators)
19
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 46
▸ expand
Error Level Analysis
9
Frequency Analysis
77
Color Distribution
30
Noise Pattern
65
Pixel Analysis
45
Compression Quality
45
Edge Consistency
48
Provenance & Metadata3 detectors · mean 45
▸ expand
ICC Profile
35
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1220 × 1285 px
Aspect
0.949
File size
157.8 KB
Bytes / pixel
0.103

Frequency Analysis

Radial1.000
DCT0.845
Upsampling1.000
Cross-channel0.223
Power-law β
-3.69
Grid energy
0.233

Edge Consistency

CV 0.546
Cell 1: 6.8328Cell 2: 10.4987Cell 3: 7.1621Cell 4: 6.2776Cell 5: 4.8217Cell 6: 21.5333Cell 7: 16.4713Cell 8: 3.8176Cell 9: 5.0787Cell 10: 18.6467Cell 11: 12.0822Cell 12: 6.0851Cell 13: 4.9286Cell 14: 14.7704Cell 15: 8.5793Cell 16: 7.3408

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

Range: 3.817621.5333

Noise Fingerprint

Variance
101.08
Std deviation
10.05
Mean
-0.0
Spatial corr.
2.411
Mean Δ
1.57
σ
2.14
CV
1.363
Uniformity
-0.363

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 16, 2026, 10:36 AM
Analyzed by@zonner