ZONN.ai Forensic Report

Case · 096ABD25 · IMAGE

Analyzed byAnonymous
ZONN Analysis
0

Very likely real

Most signals point to a real, human-captured source. Detection tools are not perfect — treat this as a strong indication, not a verdict.

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.

AI generator fingerprints detected

AI evidence

Frequency analysis (FFT score 77/100) 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).

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 · 1/100

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

  • xRayon ConvNeXtV2read real · 3/100

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

Model Agreement

53%

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)
1
xRayon ConvNeXtV2
3
Bombek1 SigLIP+DINOv2
13
INA v2 (FLUX/MJ)
36
SigLIP AI Detector
63
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 43
▸ expand
Noise Pattern
85
Color Distribution
16
Pixel Analysis
17
Error Level Analysis
18
Frequency Analysis
77
Edge Consistency
41
Compression Quality
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
900 × 900 px
Aspect
1.000
File size
85.2 KB
Bytes / pixel
0.108

Frequency Analysis

Radial1.000
DCT0.838
Upsampling1.000
Cross-channel0.235
Power-law β
-3.57
Grid energy
0.242

Edge Consistency

CV 0.764
Cell 1: 2.8549Cell 2: 8.3395Cell 3: 9.9378Cell 4: 3.6213Cell 5: 2.6917Cell 6: 9.9171Cell 7: 17.6720Cell 8: 3.2127Cell 9: 2.6421Cell 10: 7.1724Cell 11: 17.5695Cell 12: 3.5967Cell 13: 2.7745Cell 14: 3.3814Cell 15: 11.8890Cell 16: 0.9341

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

Range: 0.934117.6720

Noise Fingerprint

Variance
32.19
Std deviation
5.67
Mean
-0.0
Spatial corr.
1.608
Mean Δ
1.62
σ
1.99
CV
1.229
Uniformity
-0.229

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 15, 2026, 10:41 AM
Analyzed byAnonymous