ZONN.ai Forensic Report

Case · 2CFCEA51 · IMAGE

MAnalyzed by@muzip
ZONN Analysis
0

Probably real

More signals lean toward real than AI, but some give weaker readings. Worth a second look on close inspection.

Signal ConfidenceLimited · 45/100

Analysed Specimen

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

Heads up — 2 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.

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 (0–87). Different architectures read different feature spaces; the majority vote strengthens the consensus, but no single model is fully reliable here.

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.

  • xRayon ConvNeXtV2read real · 3/100

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

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 34
▸ expand
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
3
SigLIP AI Detector
87
Bombek1 SigLIP+DINOv2
17
CommFor (4803 Generators)
45
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 47
▸ expand
Error Level Analysis
19
Noise Pattern
76
Color Distribution
27
Frequency Analysis
73
Pixel Analysis
35
Edge Consistency
47
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

AI-typical dimensions
Dimensions
640 × 640 px
Aspect
1.000
File size
41.0 KB
Bytes / pixel
0.103

Frequency Analysis

Radial1.000
DCT0.800
Upsampling1.000
Cross-channel0.123
Power-law β
-3.31
Grid energy
0.300

Edge Consistency

CV 0.581
Cell 1: 5.5851Cell 2: 12.4505Cell 3: 4.1576Cell 4: 5.7499Cell 5: 4.6377Cell 6: 13.5656Cell 7: 13.0790Cell 8: 3.1691Cell 9: 10.0420Cell 10: 9.1111Cell 11: 18.1276Cell 12: 2.7158Cell 13: 2.5965Cell 14: 6.2678Cell 15: 16.6163Cell 16: 6.2462

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

Range: 2.596518.1276

Noise Fingerprint

Variance
61.05
Std deviation
7.81
Mean
-0.0
Spatial corr.
1.988
Mean Δ
1.54
σ
1.87
CV
1.218
Uniformity
-0.218

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 27, 2026, 8:22 PM
Analyzed by@muzip