ZONN.ai Forensic Report

Case · 5214A4CE · IMAGE

PAnalyzed by@pic_analyser35
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 · 46/100

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 72/100) 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

  • SigLIP AI Detectorread real · 1/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

  • xRayon ConvNeXtV2read real · 2/100

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

Model Agreement

67%

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 21
▸ expand
SigLIP AI Detector
1
xRayon ConvNeXtV2
2
Bombek1 SigLIP+DINOv2
18
CommFor (4803 Generators)
25
INA v2 (FLUX/MJ)
28
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 53
▸ expand
Noise Pattern
84
Color Distribution
21
Frequency Analysis
72
Error Level Analysis
32
Pixel Analysis
67
Edge Consistency
45
Compression Quality
48
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
453 × 680 px
Aspect
0.666
File size
58.0 KB
Bytes / pixel
0.193

Frequency Analysis

Radial1.000
DCT0.830
Upsampling1.000
Cross-channel0.040
Power-law β
-3.24
Grid energy
0.254

Edge Consistency

CV 0.656
Cell 1: 3.0839Cell 2: 13.5073Cell 3: 12.8912Cell 4: 2.6337Cell 5: 2.6098Cell 6: 11.0831Cell 7: 10.3624Cell 8: 2.1854Cell 9: 2.4922Cell 10: 13.6093Cell 11: 11.0509Cell 12: 2.2410Cell 13: 3.0712Cell 14: 9.1611Cell 15: 9.5557Cell 16: 2.1307

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

Range: 2.130713.6093

Noise Fingerprint

Variance
36.91
Std deviation
6.08
Mean
-0.0
Spatial corr.
1.581
Mean Δ
1.45
σ
1.48
CV
1.019
Uniformity
-0.019

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 20, 2026, 5:14 PM
Analyzed by@pic_analyser35