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Detectra v3: art/anime/meme/screenshot blind spots closed

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  1. README.md +12 -7
  2. model.onnx +1 -1
  3. model.safetensors +1 -1
README.md CHANGED
@@ -10,7 +10,7 @@ tags:
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  base_model: OwensLab/commfor-model-384
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  ---
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- # Detectra v1
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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
@@ -20,18 +20,23 @@ AI-generated), exported to single-file fp16 ONNX for ONNX Runtime Web
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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 data (WildRF train), and a
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- replay slice of CommunityForensics-Small, with web-realism augmentation
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- (JPEG cascades, resize chains). GPT-4o and Ideogram were held out 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 97.6% balanced accuracy (mangled 97.1%), modern-generator eval
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- 98.3%. Full training + evaluation code: https://github.com/ashhart/Detectra
 
 
 
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- sha256(model.onnx) = 94fac18030826da368304e0cd79ff831df3ab6e46f95463a84b19793a9d2692d
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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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