ZONN.ai Forensic Report

Case · D6CD64A9 · 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 ConfidenceModerate · 55/100

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

3 models confidently say "AI" (Ml Python Siglip, Itsnotai V2, Xrayon Convnext) while 2 confidently say "real" (Ml Commfor, Bombek1). 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 (0–100). 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)flagged AI · 100/100

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

Model Agreement

15%

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 59
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
100
SigLIP AI Detector
97
xRayon ConvNeXtV2
97
Bombek1 SigLIP+DINOv2
7
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 51
▸ expand
Noise Pattern
88
Color Distribution
18
Frequency Analysis
72
Error Level Analysis
33
Pixel Analysis
45
Compression Quality
53
Edge Consistency
49
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
32.3 KB
Bytes / pixel
0.207

Frequency Analysis

Radial1.000
DCT0.868
Upsampling1.000
Cross-channel0.023
Power-law β
-4.12
Grid energy
0.198

Edge Consistency

CV 0.518
Cell 1: 1.6148Cell 2: 1.6272Cell 3: 5.5198Cell 4: 5.5073Cell 5: 1.6137Cell 6: 2.0458Cell 7: 6.1842Cell 8: 3.6308Cell 9: 5.4769Cell 10: 3.2517Cell 11: 7.8519Cell 12: 9.3159Cell 13: 9.2855Cell 14: 5.7500Cell 15: 9.6154Cell 16: 8.4658

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

Range: 1.61379.6154

Noise Fingerprint

Variance
28.28
Std deviation
5.32
Mean
-0.0
Spatial corr.
1.126
Mean Δ
1.12
σ
1.13
CV
1.011
Uniformity
-0.011

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 19, 2026, 4:11 PM
Analyzed by@muzip