ZONN.ai Forensic Report

Case · A9C7562D · IMAGE

MAnalyzed by@muzip
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 · 51/100

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 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).

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

52%

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 18
▸ expand
CommFor (4803 Generators)
0
INA v2 (FLUX/MJ)
0
xRayon ConvNeXtV2
2
Bombek1 SigLIP+DINOv2
2
SigLIP AI Detector
54
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 51
▸ expand
Noise Pattern
93
Frequency Analysis
73
Color Distribution
29
Error Level Analysis
32
Edge Consistency
40
Pixel Analysis
45
Compression Quality
45
Provenance & Metadata3 detectors · mean 45
▸ expand
ICC Profile
35
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
736 × 920 px
Aspect
0.800
File size
60.2 KB
Bytes / pixel
0.091

Frequency Analysis

Radial1.000
DCT0.852
Upsampling1.000
Cross-channel0.075
Power-law β
-3.75
Grid energy
0.222

Edge Consistency

CV 0.795
Cell 1: 2.4445Cell 2: 2.6094Cell 3: 2.4858Cell 4: 1.9458Cell 5: 3.0364Cell 6: 3.0886Cell 7: 3.1185Cell 8: 3.2518Cell 9: 3.0237Cell 10: 6.6845Cell 11: 16.6175Cell 12: 3.4716Cell 13: 3.3458Cell 14: 3.6703Cell 15: 10.6827Cell 16: 5.3326

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

Range: 1.945816.6175

Noise Fingerprint

Variance
9.77
Std deviation
3.13
Mean
-0.0
Spatial corr.
1.149
Mean Δ
1.24
σ
1.26
CV
1.021
Uniformity
-0.021

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 25, 2026, 10:35 PM
Analyzed by@muzip