ZONN.ai Forensic Report

Case · 1B616522 · 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 · 39/100

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.

Multiple detectors unreachable

Note

2 ML detectors did not respond (Ml Python Siglip, Bombek1). The verdict was computed with reduced evidence; reliability is lower than usual.

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 (0–84). 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

  • CommFor (4803 Generators)read real · 0/100

    CommFor detector trained across 4,803 generator variants for broad coverage.

  • INA v2 (FLUX/MJ)read real · 12/100

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

Model Agreement

45%

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 41
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
12
xRayon ConvNeXtV2
84
SigLIP AI Detector
50
Bombek1 SigLIP+DINOv2
50
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 40
▸ expand
Noise Pattern
18
Error Level Analysis
26
Frequency Analysis
72
Color Distribution
30
Pixel Analysis
35
Compression Quality
48
Edge Consistency
48
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1086 × 1448 px
Aspect
0.750
File size
353.1 KB
Bytes / pixel
0.230

Frequency Analysis

Radial0.904
DCT0.707
Upsampling1.000
Cross-channel0.300
Power-law β
-2.65
Grid energy
0.440

Edge Consistency

CV 0.573
Cell 1: 10.7521Cell 2: 33.3121Cell 3: 40.3735Cell 4: 12.9771Cell 5: 23.2826Cell 6: 62.8777Cell 7: 55.5115Cell 8: 17.6003Cell 9: 14.1607Cell 10: 64.4074Cell 11: 50.4720Cell 12: 17.9605Cell 13: 14.8070Cell 14: 31.5617Cell 15: 29.0024Cell 16: 18.1401

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

Range: 10.752164.4074

Noise Fingerprint

Variance
252.54
Std deviation
15.89
Mean
-0.0
Spatial corr.
7.644
Mean Δ
3.21
σ
3.56
CV
1.109
Uniformity
-0.109

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 13, 2026, 12:18 PM
Analyzed byAnonymous