ZONN.ai Forensic Report

Case · 95F43283 · IMAGE

MAnalyzed by@muzip
ZONN Analysis
0

Very likely real

Most signals point to a real, human-captured source. Detection tools are not perfect — treat this as a strong indication, not a verdict.

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.

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).

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.

  • INA v2 (FLUX/MJ)read real · 2/100

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

Model Agreement

54%

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 20
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SigLIP AI Detector
0
INA v2 (FLUX/MJ)
2
xRayon ConvNeXtV2
3
Bombek1 SigLIP+DINOv2
13
CommFor (4803 Generators)
54
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 57
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Noise Pattern
90
Pixel Analysis
85
Error Level Analysis
22
Frequency Analysis
70
Color Distribution
36
Compression Quality
48
Edge Consistency
51
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
600 × 472 px
Aspect
1.271
File size
46.2 KB
Bytes / pixel
0.167

Frequency Analysis

Radial0.978
DCT0.705
Upsampling1.000
Cross-channel0.119
Power-law β
-2.73
Grid energy
0.442

Edge Consistency

CV 0.488
Cell 1: 2.7854Cell 2: 4.8397Cell 3: 4.7316Cell 4: 3.0999Cell 5: 1.9414Cell 6: 8.7813Cell 7: 4.5890Cell 8: 3.0371Cell 9: 3.1801Cell 10: 12.1625Cell 11: 10.1793Cell 12: 4.8602Cell 13: 5.9872Cell 14: 7.8043Cell 15: 7.0543Cell 16: 7.8054

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

Range: 1.941412.1625

Noise Fingerprint

Variance
15.45
Std deviation
3.93
Mean
-0.0
Spatial corr.
1.427
Mean Δ
1.16
σ
1.35
CV
1.170
Uniformity
-0.170

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 18, 2026, 5:17 PM
Analyzed by@muzip