ZONN.ai Forensic Report

Case · 3FDF4475 · IMAGE · X

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 73/100) 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 (0–96). 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.

  • INA v2 (FLUX/MJ)read real · 0/100

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

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 26
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
0
Bombek1 SigLIP+DINOv2
2
SigLIP AI Detector
96
xRayon ConvNeXtV2
7
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Noise Pattern
80
Color Distribution
26
Frequency Analysis
73
Error Level Analysis
31
Compression Quality
40
Pixel Analysis
45
Edge Consistency
51
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
959 × 720 px
Aspect
1.332
File size
81.4 KB
Bytes / pixel
0.121

Frequency Analysis

Radial1.000
DCT0.890
Upsampling1.000
Cross-channel0.024
Power-law β
-4.43
Grid energy
0.164

Edge Consistency

CV 0.474
Cell 1: 5.1153Cell 2: 4.2958Cell 3: 5.2179Cell 4: 6.7550Cell 5: 13.7605Cell 6: 14.7166Cell 7: 13.2819Cell 8: 10.2573Cell 9: 10.1689Cell 10: 10.4814Cell 11: 8.8048Cell 12: 7.6174Cell 13: 5.4248Cell 14: 3.0985Cell 15: 3.0412Cell 16: 4.2197

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

Range: 3.041214.7166

Noise Fingerprint

Variance
49.04
Std deviation
7.00
Mean
-0.0
Spatial corr.
1.817
Mean Δ
1.29
σ
1.33
CV
1.032
Uniformity
-0.032

Provenance

Source Dossier

PlatformX
AuthorFxTwitter
Content Typeimage
Analyzed OnMay 14, 2026, 9:20 AM
Analyzed byAnonymous