ZONN.ai Forensic Report

Case · DA8895B4 · 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 · 48/100

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 (3–100). 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 · 3/100

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

Model Agreement

33%

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 34
▸ expand
INA v2 (FLUX/MJ)
100
xRayon ConvNeXtV2
3
Bombek1 SigLIP+DINOv2
6
SigLIP AI Detector
14
CommFor (4803 Generators)
33
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 50
▸ expand
Noise Pattern
80
Frequency Analysis
72
Color Distribution
30
Error Level Analysis
31
Pixel Analysis
35
Compression Quality
55
Edge Consistency
47
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
800 × 800 px
Aspect
1.000
File size
809.7 KB
Bytes / pixel
1.296

Frequency Analysis

Radial1.000
DCT0.812
Upsampling1.000
Cross-channel0.056
Power-law β
-3.42
Grid energy
0.281

Edge Consistency

CV 0.595
Cell 1: 2.4523Cell 2: 18.7582Cell 3: 16.6183Cell 4: 3.0719Cell 5: 3.6438Cell 6: 14.8235Cell 7: 16.2907Cell 8: 2.2637Cell 9: 3.6876Cell 10: 9.4935Cell 11: 10.7801Cell 12: 3.6357Cell 13: 7.3115Cell 14: 9.9467Cell 15: 12.1977Cell 16: 14.3036

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

Range: 2.263718.7582

Noise Fingerprint

Variance
42.15
Std deviation
6.49
Mean
-0.0
Spatial corr.
2.263
Mean Δ
1.66
σ
1.71
CV
1.033
Uniformity
-0.033

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 15, 2026, 11:57 AM
Analyzed byAnonymous