ZONN.ai Forensic Report

Case · 4B43580D · IMAGE

Analyzed byAnonymous
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 · 54/100

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.

AI generator fingerprints detected

AI evidence

Frequency analysis (FFT score 76/100) 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.

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 (0–99). 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

  • CommFor (4803 Generators)read real · 0/100

    CommFor detector trained across 4,803 generator variants for broad coverage.

  • Error Level Analysisread real · 0/100

    Error Level Analysis. Re-saves and diffs to expose uneven compression regions.

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 28
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
99
xRayon ConvNeXtV2
2
Bombek1 SigLIP+DINOv2
6
SigLIP AI Detector
9
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 43
▸ expand
Error Level Analysis
0
Color Distribution
9
Noise Pattern
84
Frequency Analysis
76
Edge Consistency
44
Pixel Analysis
45
Compression Quality
45
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
700 × 900 px
Aspect
0.778
File size
67.4 KB
Bytes / pixel
0.110

Frequency Analysis

Radial1.000
DCT0.819
Upsampling1.000
Cross-channel0.205
Power-law β
-3.46
Grid energy
0.271

Edge Consistency

CV 0.692
Cell 1: 0.6815Cell 2: 4.7762Cell 3: 3.2017Cell 4: 3.9717Cell 5: 0.8022Cell 6: 4.9991Cell 7: 11.2259Cell 8: 8.2177Cell 9: 5.2018Cell 10: 13.4135Cell 11: 5.9108Cell 12: 4.3904Cell 13: 19.0633Cell 14: 5.8367Cell 15: 7.3162Cell 16: 5.6467

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

Range: 0.681519.0633

Noise Fingerprint

Variance
39.77
Std deviation
6.31
Mean
-0.0
Spatial corr.
1.505
Mean Δ
1.39
σ
2.08
CV
1.493
Uniformity
-0.493

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 14, 2026, 8:49 AM
Analyzed byAnonymous