---
license: mit
library_name: onnx
tags:
  - image-classification
  - ai-image-detection
  - deepfake-detection
  - onnx
  - webgpu
base_model: OwensLab/commfor-model-384
---

# Detectra v3

On-device AI-image detector powering the [Detectra Chrome extension](https://github.com/ashhart/Detectra):
a ViT-S/16 @384 binary classifier (`sigmoid(logit)` = probability the image is
AI-generated), exported to single-file fp16 ONNX for ONNX Runtime Web
(WebGPU/WASM).

Fine-tuned from the MIT-licensed
[Community Forensics ViT-S](https://huggingface.co/OwensLab/commfor-model-384)
(Park & Owens, CVPR 2025) on modern generators (DALL·E 3, Midjourney, Flux,
SD3.5, Recraft, HiDream), in-the-wild social media (WildRF train), and a
replay slice of CommunityForensics-Small — plus, new in v3, non-photographic
REAL classes that defeat most detectors: human paintings (WikiArt), hand-drawn
anime, meme composites and webpage screenshots. GPT-4o and Ideogram were held
out of training entirely.

Preprocessing: shortest edge → 440 (bilinear) → center-crop 384 → [0,1] →
ImageNet normalize. Input `pixel_values` 1×3×384×384 fp32, output `logit` 1×1.

Held-out results through the extension pipeline @0.65 threshold:
WildRF-test 97.0% balanced accuracy (mangled 96.9%), modern-generator eval
99.4% BA (TPR 98.9% incl. never-trained GPT-4o/Ideogram). Per-category TNR on
difficult reals: paintings 99.7% (held-out n=1,200), hand-drawn anime 100%, memes 97.6%, webpage
screenshots 100%. Full training + evaluation code:
https://github.com/ashhart/Detectra

sha256(model.onnx) = 1414b9aafaa01a644ed706224973f09b53a1388282104409862df9893b1b962b

## Files
- model.onnx — fp16 ONNX export (deployed by the extension)
- model.safetensors — fp32 fine-tuned checkpoint (for further training/audit)