ZONN.ai Forensic Report

Case · C0A8CA81 · IMAGE

MAnalyzed by@muzip
ZONN Analysis
0

Probably AI-generated

More signals lean toward AI generation than real, but some give weaker readings. Treat with caution.

Signal ConfidenceLimited · 49/100

Analysed Specimen

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

Heads up — 5 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.

No metadata at all

AI evidence

The file has no EXIF, XMP, IPTC, or ICC metadata. This is common for social-media re-uploads and for many generator outputs — real camera files almost always carry an ICC profile.

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 (0–95). Different architectures read different feature spaces; the majority vote strengthens the consensus, but no single model is fully reliable here.

No ICC color profile

AI evidence

The image does not embed an ICC color profile. Real camera files almost always carry sRGB or Adobe RGB profiles — a missing profile is often a sign of generator output or re-encoded media.

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 · 0/100

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

Model Agreement

32%

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 32
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
95
Bombek1 SigLIP+DINOv2
8
SigLIP AI Detector
41
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Noise Pattern
86
Color Distribution
15
Error Level Analysis
24
Frequency Analysis
71
Pixel Analysis
45
Compression Quality
53
Edge Consistency
48
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
400 × 400 px
Aspect
1.000
File size
32.6 KB
Bytes / pixel
0.209

Frequency Analysis

Radial1.000
DCT0.819
Upsampling1.000
Cross-channel0.039
Power-law β
-3.68
Grid energy
0.272

Edge Consistency

CV 0.572
Cell 1: 4.4573Cell 2: 5.3833Cell 3: 1.2175Cell 4: 6.7583Cell 5: 5.2903Cell 6: 6.2524Cell 7: 5.5060Cell 8: 16.1949Cell 9: 6.3681Cell 10: 4.5192Cell 11: 7.1683Cell 12: 10.7261Cell 13: 1.6764Cell 14: 2.2227Cell 15: 6.0180Cell 16: 9.4158

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

Range: 1.217516.1949

Noise Fingerprint

Variance
33.11
Std deviation
5.75
Mean
-0.0
Spatial corr.
1.365
Mean Δ
1.14
σ
1.29
CV
1.140
Uniformity
-0.140

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 19, 2026, 4:58 PM
Analyzed by@muzip