ZONN.ai Forensic Report

Case · 8896242C · 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 · 54/100

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 (0–97). 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

  • 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 · 1/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

Model Agreement

29%

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 28
▸ expand
INA v2 (FLUX/MJ)
0
SigLIP AI Detector
1
CommFor (4803 Generators)
2
xRayon ConvNeXtV2
97
Bombek1 SigLIP+DINOv2
15
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 48
▸ expand
Noise Pattern
90
Error Level Analysis
19
Color Distribution
27
Frequency Analysis
71
Pixel Analysis
35
Edge Consistency
43
Compression Quality
53
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
29.6 KB
Bytes / pixel
0.190

Frequency Analysis

Radial1.000
DCT0.824
Upsampling1.000
Cross-channel0.018
Power-law β
-3.62
Grid energy
0.264

Edge Consistency

CV 0.713
Cell 1: 2.0394Cell 2: 5.9714Cell 3: 3.1903Cell 4: 0.8374Cell 5: 5.2426Cell 6: 5.0178Cell 7: 6.5002Cell 8: 2.1505Cell 9: 3.5740Cell 10: 4.6537Cell 11: 6.3989Cell 12: 13.1205Cell 13: 1.5505Cell 14: 3.2871Cell 15: 4.6678Cell 16: 14.6395

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

Range: 0.837414.6395

Noise Fingerprint

Variance
19.46
Std deviation
4.41
Mean
-0.0
Spatial corr.
1.134
Mean Δ
0.98
σ
1.20
CV
1.220
Uniformity
-0.220

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 19, 2026, 9:14 PM
Analyzed by@muzip