ZONN.ai Forensic Report

Case · 3400CEEA · IMAGE

ZAnalyzed by@zonner
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 · 48/100

Analysed Specimen

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

Heads up — 4 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.

Image is very small

Note

Dimensions 196×194 (under 256×256). Detection models cannot give a reliable answer on inputs this small.

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 (0–74). 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 · 2/100

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

Model Agreement

38%

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 32
▸ expand
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
2
Bombek1 SigLIP+DINOv2
2
SigLIP AI Detector
74
CommFor (4803 Generators)
62
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 54
▸ expand
Noise Pattern
97
Color Distribution
10
Frequency Analysis
73
Error Level Analysis
31
Pixel Analysis
57
Edge Consistency
56
Compression Quality
53
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
196 × 194 px
Aspect
1.010
File size
8.1 KB
Bytes / pixel
0.218

Frequency Analysis

Radial1.000
DCT0.872
Upsampling1.000
Cross-channel0.038
Power-law β
-4.20
Grid energy
0.192

Edge Consistency

CV 0.381
Cell 1: 1.1067Cell 2: 2.2513Cell 3: 1.4197Cell 4: 4.0642Cell 5: 2.2217Cell 6: 1.8663Cell 7: 1.6108Cell 8: 4.6591Cell 9: 2.3539Cell 10: 1.6894Cell 11: 1.7615Cell 12: 2.7234Cell 13: 3.4100Cell 14: 2.3217Cell 15: 2.8258Cell 16: 2.7330

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

Range: 1.10674.6591

Noise Fingerprint

Variance
5.63
Std deviation
2.37
Mean
0.0
Spatial corr.
0.455
Mean Δ
0.80
σ
0.83
CV
1.038
Uniformity
-0.038

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 16, 2026, 10:43 AM
Analyzed by@zonner