ZONN.ai Forensic Report

Case · 2E737B0D · IMAGE

WAnalyzed by@walena
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 · 42/100

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.

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).

Uneven compression (ELA)

AI evidence

ELA coefficient of variation is 1.58 — different regions show noticeably different compression levels. Common with composites, edits, or AI generations.

High pixel-level noise

Note

Heavy but spatially uncorrelated noise was detected. This can be camera sensor noise or synthetic dithering — by itself it is not a strong AI/real signal.

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.

  • Error Level Analysisread real · 0/100

    Error Level Analysis. Re-saves and diffs to expose uneven compression regions.

Model Agreement

47%

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 37
▸ expand
INA v2 (FLUX/MJ)
0
Bombek1 SigLIP+DINOv2
9
SigLIP AI Detector
79
CommFor (4803 Generators)
32
xRayon ConvNeXtV2
49
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 36
▸ expand
Error Level Analysis
0
Pixel Analysis
17
Color Distribution
25
Frequency Analysis
73
Noise Pattern
42
Compression Quality
45
Edge Consistency
51
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
1389 × 763 px
Aspect
1.820
File size
109.1 KB
Bytes / pixel
0.105

Frequency Analysis

Radial1.000
DCT0.804
Upsampling1.000
Cross-channel0.123
Power-law β
-3.44
Grid energy
0.294

Edge Consistency

CV 0.478
Cell 1: 8.4705Cell 2: 8.6328Cell 3: 11.0466Cell 4: 13.6661Cell 5: 7.1540Cell 6: 5.8265Cell 7: 1.9943Cell 8: 2.8157Cell 9: 12.9158Cell 10: 4.3089Cell 11: 2.5475Cell 12: 11.7626Cell 13: 6.6906Cell 14: 6.8449Cell 15: 6.8982Cell 16: 13.0873

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

Range: 1.994313.6661

Noise Fingerprint

Variance
6242.63
Std deviation
79.01
Mean
27.4
Spatial corr.
2.008
Mean Δ
1.37
σ
2.18
CV
1.585
Uniformity
-0.585

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 15, 2026, 8:49 PM
Analyzed by@walena