ZONN.ai Forensic Report

Case · 8B05A85A · 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 · 44/100

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 75/100) 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 · 3/100

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

Model Agreement

50%

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 27
▸ expand
SigLIP AI Detector
1
xRayon ConvNeXtV2
3
Bombek1 SigLIP+DINOv2
4
INA v2 (FLUX/MJ)
62
CommFor (4803 Generators)
44
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
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Color Distribution
11
Noise Pattern
87
Error Level Analysis
25
Frequency Analysis
75
Pixel Analysis
45
Compression Quality
48
Edge Consistency
50
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
401 × 601 px
Aspect
0.667
File size
59.5 KB
Bytes / pixel
0.253

Frequency Analysis

Radial1.000
DCT0.830
Upsampling1.000
Cross-channel0.180
Power-law β
-3.50
Grid energy
0.255

Edge Consistency

CV 0.499
Cell 1: 6.6693Cell 2: 9.3507Cell 3: 7.6716Cell 4: 3.7947Cell 5: 5.7534Cell 6: 7.6143Cell 7: 5.4617Cell 8: 2.8571Cell 9: 9.2117Cell 10: 6.9512Cell 11: 3.5533Cell 12: 1.7942Cell 13: 13.6769Cell 14: 12.2488Cell 15: 6.3250Cell 16: 2.3925

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

Range: 1.794213.6769

Noise Fingerprint

Variance
26.97
Std deviation
5.19
Mean
-0.0
Spatial corr.
1.469
Mean Δ
1.63
σ
1.82
CV
1.121
Uniformity
-0.121

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 29, 2026, 10:54 PM
Analyzed byAnonymous