ZONN.ai Forensic Report

Case · 4DCAD237 · IMAGE

MAnalyzed by@muzip
ZONN Analysis
0

Very likely AI-generated

Most signals point to AI generation. Detection tools are not perfect — treat this as a strong indication, not a verdict.

Signal ConfidenceLimited · 52/100

Analysed Specimen

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

Heads up — 3 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.

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).

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)flagged AI · 100/100

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

  • SigLIP AI Detectorflagged AI · 99/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

Model Agreement

39%

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 78
▸ expand
INA v2 (FLUX/MJ)
100
SigLIP AI Detector
99
xRayon ConvNeXtV2
98
Bombek1 SigLIP+DINOv2
97
CommFor (4803 Generators)
22
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Error Level Analysis
19
Frequency Analysis
77
Noise Pattern
71
Color Distribution
29
Edge Consistency
35
Pixel Analysis
57
Compression Quality
53
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

AI-typical dimensions
Dimensions
1024 × 1024 px
Aspect
1.000
File size
112.0 KB
Bytes / pixel
0.109

Frequency Analysis

Radial1.000
DCT0.823
Upsampling1.000
Cross-channel0.243
Power-law β
-3.16
Grid energy
0.265

Edge Consistency

CV 0.890
Cell 1: 3.3420Cell 2: 10.4869Cell 3: 16.2427Cell 4: 2.8776Cell 5: 7.3196Cell 6: 39.2698Cell 7: 27.1978Cell 8: 2.8668Cell 9: 2.8564Cell 10: 12.6992Cell 11: 5.0120Cell 12: 3.0995Cell 13: 3.0661Cell 14: 11.6120Cell 15: 12.3101Cell 16: 21.1826

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

Range: 2.856439.2698

Noise Fingerprint

Variance
69.15
Std deviation
8.32
Mean
-0.0
Spatial corr.
2.756
Mean Δ
1.87
σ
2.29
CV
1.222
Uniformity
-0.222

Provenance

Source Dossier

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