Detectra v3: art/anime/meme/screenshot blind spots closed
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- model.safetensors +1 -1
README.md
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base_model: OwensLab/commfor-model-384
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---
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# Detectra
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On-device AI-image detector powering the [Detectra Chrome extension](https://github.com/ashhart/Detectra):
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a ViT-S/16 @384 binary classifier (`sigmoid(logit)` = probability the image is
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Fine-tuned from the MIT-licensed
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[Community Forensics ViT-S](https://huggingface.co/OwensLab/commfor-model-384)
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(Park & Owens, CVPR 2025) on modern generators (DALL·E 3, Midjourney, Flux,
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SD3.5, Recraft, HiDream), in-the-wild social
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replay slice of CommunityForensics-Small,
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Preprocessing: shortest edge → 440 (bilinear) → center-crop 384 → [0,1] →
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ImageNet normalize. Input `pixel_values` 1×3×384×384 fp32, output `logit` 1×1.
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Held-out results through the extension pipeline @0.65 threshold:
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WildRF-test 97.
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sha256(model.onnx) =
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## Files
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- model.onnx — fp16 ONNX export (deployed by the extension)
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base_model: OwensLab/commfor-model-384
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---
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# Detectra v3
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On-device AI-image detector powering the [Detectra Chrome extension](https://github.com/ashhart/Detectra):
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a ViT-S/16 @384 binary classifier (`sigmoid(logit)` = probability the image is
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Fine-tuned from the MIT-licensed
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[Community Forensics ViT-S](https://huggingface.co/OwensLab/commfor-model-384)
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(Park & Owens, CVPR 2025) on modern generators (DALL·E 3, Midjourney, Flux,
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SD3.5, Recraft, HiDream), in-the-wild social media (WildRF train), and a
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replay slice of CommunityForensics-Small — plus, new in v3, non-photographic
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REAL classes that defeat most detectors: human paintings (WikiArt), hand-drawn
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anime, meme composites and webpage screenshots. GPT-4o and Ideogram were held
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out of training entirely.
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Preprocessing: shortest edge → 440 (bilinear) → center-crop 384 → [0,1] →
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ImageNet normalize. Input `pixel_values` 1×3×384×384 fp32, output `logit` 1×1.
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Held-out results through the extension pipeline @0.65 threshold:
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WildRF-test 97.0% balanced accuracy (mangled 96.9%), modern-generator eval
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99.4% BA (TPR 98.9% incl. never-trained GPT-4o/Ideogram). Per-category TNR on
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difficult reals: paintings 99.8%, hand-drawn anime 100%, memes 97.6%, webpage
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screenshots 100%. Full training + evaluation code:
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https://github.com/ashhart/Detectra
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sha256(model.onnx) = 1414b9aafaa01a644ed706224973f09b53a1388282104409862df9893b1b962b
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## Files
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- model.onnx — fp16 ONNX export (deployed by the extension)
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model.onnx
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model.safetensors
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