ZONN.ai Forensic Report

Case · 88EBD793 · IMAGE

KAnalyzed by@kcangnl_7e3c6
ZONN Analysis
0

Probably AI-generated

More signals lean toward AI generation than real, but some give weaker readings. Treat with caution.

Signal ConfidenceLimited · 49/100

Analysed Specimen

Original analysed image
Forensic suspicion heatmap
OriginalHeatmap
POS55/100
No flagged regions

Heads up — 5 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.

ML detectors disagree with each other

Note

2 models confidently say "AI" (Ml Python Siglip, Xrayon Convnext) while 2 confidently say "real" (Itsnotai V2, Bombek1). This image sits at the edge of what ML can decide — manual review is recommended.

No metadata at all

AI evidence

The file has no EXIF, XMP, IPTC, or ICC metadata. This is common for social-media re-uploads and for many generator outputs — real camera files almost always carry an ICC profile.

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 (9–100). Different architectures read different feature spaces; the majority vote strengthens the consensus, but no single model is fully reliable here.

No ICC color profile

AI evidence

The image does not embed an ICC color profile. Real camera files almost always carry sRGB or Adobe RGB profiles — a missing profile is often a sign of generator output or re-encoded media.

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 Detectorflagged AI · 100/100

    SigLIP visual-language model probing semantic vs perceptual coherence.

  • Noise Patternflagged AI · 98/100

    Sensor-noise (PRNU) fingerprint check. Real cameras leave camera-unique noise.

Model Agreement

29%

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 55
▸ expand
SigLIP AI Detector
100
xRayon ConvNeXtV2
97
INA v2 (FLUX/MJ)
9
Bombek1 SigLIP+DINOv2
14
CommFor (4803 Generators)
62
Manipulation Map (IML-ViT)
50
Pixel & Frequency Forensics7 detectors · mean 53
▸ expand
Noise Pattern
98
Color Distribution
10
Frequency Analysis
76
Error Level Analysis
29
Pixel Analysis
57
Edge Consistency
56
Compression Quality
45
Provenance & Metadata3 detectors · mean 54
▸ expand
ICC Profile
62
Metadata
50
C2PA Provenance
50

Image Quality

Dimensions
766 × 432 px
Aspect
1.773
File size
373.5 KB
Bytes / pixel
1.156

Frequency Analysis

Radial1.000
DCT0.902
Upsampling1.000
Cross-channel0.151
Power-law β
-4.23
Grid energy
0.147

Edge Consistency

CV 0.386
Cell 1: 0.6984Cell 2: 1.2567Cell 3: 1.8298Cell 4: 1.8664Cell 5: 1.5359Cell 6: 2.8018Cell 7: 1.6412Cell 8: 1.7979Cell 9: 1.2150Cell 10: 2.1005Cell 11: 0.4917Cell 12: 1.8057Cell 13: 1.1335Cell 14: 0.8596Cell 15: 1.1572Cell 16: 2.2899

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

Range: 0.49172.8018

Noise Fingerprint

Variance
1.67
Std deviation
1.29
Mean
-0.0
Spatial corr.
0.333
Mean Δ
0.90
σ
0.96
CV
1.066
Uniformity
-0.066

Provenance

Source Dossier

PlatformDirect upload
Author
Content Typeimage
Analyzed OnJun 1, 2026, 1:51 PM
Analyzed by@kcangnl_7e3c6