ZONN.ai Forensic Report

Case · 1F1FDC6B · 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 71/100) 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

  • INA v2 (FLUX/MJ)read real · 0/100

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

  • SigLIP AI Detectorread real · 2/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

Model Agreement

56%

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 19
▸ expand
INA v2 (FLUX/MJ)
0
SigLIP AI Detector
2
CommFor (4803 Generators)
5
Bombek1 SigLIP+DINOv2
9
xRayon ConvNeXtV2
50
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
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Noise Pattern
90
Error Level Analysis
20
Color Distribution
26
Frequency Analysis
71
Pixel Analysis
35
Compression Quality
53
Edge Consistency
49
Provenance & Metadata3 detectors · mean 54
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ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
400 × 400 px
Aspect
1.000
File size
30.2 KB
Bytes / pixel
0.194

Frequency Analysis

Radial1.000
DCT0.820
Upsampling1.000
Cross-channel0.028
Power-law β
-3.66
Grid energy
0.270

Edge Consistency

CV 0.543
Cell 1: 3.8781Cell 2: 6.0052Cell 3: 3.9081Cell 4: 1.1029Cell 5: 7.1635Cell 6: 6.2740Cell 7: 7.3531Cell 8: 5.6118Cell 9: 2.6934Cell 10: 3.5716Cell 11: 5.8199Cell 12: 12.3994Cell 13: 1.4867Cell 14: 2.7013Cell 15: 4.7714Cell 16: 9.5542

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

Range: 1.102912.3994

Noise Fingerprint

Variance
19.95
Std deviation
4.47
Mean
-0.0
Spatial corr.
1.164
Mean Δ
1.01
σ
1.21
CV
1.196
Uniformity
-0.196

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 19, 2026, 9:16 PM
Analyzed by@muzip