ZONN.ai Forensic Report

Case · ADACAE45 · IMAGE

MAnalyzed by@muzip
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 · 38/100

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.

Heavy JPEG compression

Note

Bytes-per-pixel is 0.033 — the image is heavily compressed. ML accuracy drops on heavily re-encoded images; treat the verdict with extra caution.

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 (0–97). 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

  • CommFor (4803 Generators)read real · 0/100

    CommFor detector trained across 4,803 generator variants for broad coverage.

  • Error Level Analysisread real · 2/100

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

Model Agreement

32%

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 45
▸ expand
CommFor (4803 Generators)
0
SigLIP AI Detector
97
INA v2 (FLUX/MJ)
8
xRayon ConvNeXtV2
72
Bombek1 SigLIP+DINOv2
41
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 49
▸ expand
Error Level Analysis
2
Noise Pattern
97
Color Distribution
24
Pixel Analysis
75
Frequency Analysis
71
Edge Consistency
35
Compression Quality
40
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
800 × 600 px
Aspect
1.333
File size
15.2 KB
Bytes / pixel
0.033

Frequency Analysis

Radial1.000
DCT0.802
Upsampling1.000
Cross-channel0.032
Power-law β
-3.56
Grid energy
0.296

Edge Consistency

CV 1.017
Cell 1: 0.5170Cell 2: 0.7577Cell 3: 0.8628Cell 4: 0.7333Cell 5: 0.7352Cell 6: 0.7181Cell 7: 0.9776Cell 8: 1.1375Cell 9: 1.0279Cell 10: 1.5267Cell 11: 1.3920Cell 12: 0.8161Cell 13: 1.5761Cell 14: 6.0426Cell 15: 5.7294Cell 16: 1.2968

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

Range: 0.51706.0426

Noise Fingerprint

Variance
4.38
Std deviation
2.09
Mean
0.0
Spatial corr.
0.368
Mean Δ
0.59
σ
0.87
CV
1.475
Uniformity
-0.475

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnMay 18, 2026, 5:15 PM
Analyzed by@muzip