ZONN.ai Forensic Report

Case · 9F64FB9A · 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 · 46/100

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.

ML detectors disagree with each other

Note

2 models confidently say "AI" (Xrayon Convnext, Bombek1) while 2 confidently say "real" (Ml Python Siglip, Itsnotai V2). This image sits at the edge of what ML can decide — manual review is recommended.

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 (5–97). 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

  • xRayon ConvNeXtV2flagged AI · 97/100

    ConvNeXtV2 detector trained on FLUX, DALL-E 3, SDXL, SD3.5, and Midjourney v6.

  • SigLIP AI Detectorread real · 5/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

Model Agreement

28%

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 49
▸ expand
xRayon ConvNeXtV2
97
SigLIP AI Detector
5
INA v2 (FLUX/MJ)
6
Bombek1 SigLIP+DINOv2
89
CommFor (4803 Generators)
45
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 43
▸ expand
Error Level Analysis
26
Frequency Analysis
74
Noise Pattern
28
Pixel Analysis
35
Color Distribution
35
Compression Quality
56
Edge Consistency
49
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
168.6 KB
Bytes / pixel
0.165

Frequency Analysis

Radial1.000
DCT0.779
Upsampling1.000
Cross-channel0.194
Power-law β
-3.17
Grid energy
0.331

Edge Consistency

CV 0.542
Cell 1: 22.9661Cell 2: 11.1069Cell 3: 25.8376Cell 4: 18.5213Cell 5: 34.0795Cell 6: 47.2230Cell 7: 33.0168Cell 8: 32.6926Cell 9: 19.6646Cell 10: 30.0185Cell 11: 23.8017Cell 12: 26.5422Cell 13: 3.8694Cell 14: 2.5529Cell 15: 6.6597Cell 16: 13.4638

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

Range: 2.552947.2230

Noise Fingerprint

Variance
212.20
Std deviation
14.57
Mean
-0.0
Spatial corr.
5.370
Mean Δ
2.46
σ
2.74
CV
1.115
Uniformity
-0.115

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 18, 2026, 5:13 PM
Analyzed by@muzip