| --- |
| license: mit |
| tags: |
| - deepfake-detection |
| - ai-image-detection |
| - dfd-arena |
| - bitmind |
| library_name: transformers |
| pipeline_tag: image-classification |
| --- |
| |
| # PixelPrism v0.1 — DFD Arena Submission |
|
|
| Sanitized single-detector submission for the [BitMind Deepfake Detection Arena](https://huggingface.co/spaces/bitmind/dfd-arena-leaderboard). |
|
|
| This repo represents the **most informative single component** of PixelPrism's |
| production [V9 16-detector ensemble](https://pixelprism.ai/leaderboard), 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](https://huggingface.co/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_flux` uses FLUX.1-schnell (non-commercial license) |
| - `dire_sdxl` uses Stability SDXL (CreativeML OpenRAIL-M) |
| - `face` uses 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](https://huggingface.co/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). |
|
|
