ZONN.ai Forensic Report

Case · 780B5B4D · IMAGE

WAnalyzed by@walena
ZONN Analysis
0

Inconclusive

Signals are mixed or weak. We can't tell with confidence — context, source, and your own judgement matter here.

Signal ConfidenceLimited · 46/100

Analysed Specimen

Original analysed image
Forensic suspicion heatmap
OriginalHeatmap
POS55/100
No flagged regions

Heads up — 4 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.

ML detectors disagree with each other

Note

2 models confidently say "AI" (Itsnotai V2, Xrayon Convnext) while 2 confidently say "real" (Ml Python Siglip, Bombek1). This image sits at the edge of what ML can decide — manual review is recommended.

Just above the real threshold

Note

The score (45/100) is between real (40) and inconclusive. There is not enough confidence to call this a clean "real" verdict.

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 (0–93). 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

  • SigLIP AI Detectorread real · 0/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

  • Color Distributionread real · 6/100

    Global color distribution shape vs natural-photo baselines.

Model Agreement

30%

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 47
▸ expand
SigLIP AI Detector
0
xRayon ConvNeXtV2
93
Bombek1 SigLIP+DINOv2
11
INA v2 (FLUX/MJ)
87
CommFor (4803 Generators)
38
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Color Distribution
6
Noise Pattern
78
Frequency Analysis
71
Error Level Analysis
32
Pixel Analysis
57
Edge Consistency
51
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
371 × 249 px
Aspect
1.490
File size
151.2 KB
Bytes / pixel
1.676

Frequency Analysis

Radial1.000
DCT0.843
Upsampling1.000
Cross-channel0.003
Power-law β
-3.39
Grid energy
0.236

Edge Consistency

CV 0.472
Cell 1: 1.9658Cell 2: 6.0319Cell 3: 8.9153Cell 4: 4.9201Cell 5: 9.5200Cell 6: 12.9993Cell 7: 10.9253Cell 8: 10.3652Cell 9: 4.7097Cell 10: 15.8572Cell 11: 12.7567Cell 12: 6.4326Cell 13: 3.1555Cell 14: 11.3901Cell 15: 12.4656Cell 16: 3.7515

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

Range: 1.965815.8572

Noise Fingerprint

Variance
62.16
Std deviation
7.88
Mean
-0.0
Spatial corr.
1.539
Mean Δ
1.15
σ
1.17
CV
1.022
Uniformity
-0.022

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 15, 2026, 8:50 PM
Analyzed by@walena