PixelPrism v0.1 — DFD Arena Submission
Sanitized single-detector submission for the BitMind Deepfake Detection Arena.
This repo represents the most informative single component of PixelPrism's
production V9 16-detector ensemble, wrapped
in the BitMind DeepfakeDetector interface so it can be evaluated alongside
NPR / UCF / CAMO on the public leaderboard.
What's in this submission
A wrapper around the Swin V2 transformer head (haywoodsloan/ai-image-detector-deploy, MIT licensed). In PixelPrism's V9 permutation-importance audit (8000 samples, 5 reps), Swin V2 ranked #1 by a wide margin at importance 0.271, vs 0.109 for the next-best detector (vit3) and 0.032 for DIRE-FLUX. It alone accounts for ~38% of V9's total discriminative power.
What's NOT in this submission
The full PixelPrism V9 ensemble fuses 16 detectors via a
HistGradientBoostingClassifier meta-classifier:
fft, vit, vit2, vit3, dire (SD 1.5), clip, srm, exif, face,
cfa, prnu, c2pa, anatomy, swin, dire_sdxl, dire_flux
Some V9 components depend on weights that are not MIT-redistributable:
dire_fluxuses FLUX.1-schnell (non-commercial license)dire_sdxluses Stability SDXL (CreativeML OpenRAIL-M)faceuses FaceForensics++ Xception variants (access-gated)
Those stay in our internal production stack rather than the public submission.
Live full-ensemble numbers
The full V9 ensemble is live at https://pixelprism.ai/api/detect (paid) and https://pixelprism.ai/api/scan-public (5/day free tier). Per-generator detection rates and 30-day drift trend are published at https://pixelprism.ai/leaderboard (refreshed monthly with each retrain).
V9 internal holdout (8000 stratified samples, 4000 real / 4000 AI):
| Metric | V9 |
|---|---|
| Overall | 96.7% |
| Real | 96.1% |
| AI | 97.4% |
| Per-generator min | 91.0% (Grok) |
| Drift gap (fresh AI vs known AI) | −2.7pp (fresh AI now BEATS known AI) |
Files in this repo
| File | Purpose |
|---|---|
pixelprism_detector.py |
The DeepfakeDetector subclass registered as PixelPrism in DETECTOR_REGISTRY |
pixelprism_config.yaml |
YAML config with hf_repo, backbone_repo, ai_label_idx |
model.safetensors |
Swin V2 weights (re-hosted, byte-identical to upstream) |
config.json |
Swin V2 model config |
preprocessor_config.json |
Swin V2 image preprocessor config |
README.md |
This file |
Citation / contact
If you use this in research or a comparison study, cite:
- The Swin V2 detector: haywoodsloan/ai-image-detector-deploy
- PixelPrism's full ensemble methodology: https://pixelprism.ai/leaderboard
Operator: Chris Crawley, PixelPrism.ai · https://pixelprism.ai
License
MIT (matches the upstream Swin V2 model + matches the BitMind DFD Arena requirement).