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Confirms that CLIP-based detection is biased toward generators that use CLIP internally. Tested on Defactify MS-COCOAI dataset (96K images, 5 labeled generators, semantically matched captions): Generator Uses CLIP? Hand-crafted CLIP Delta SD 2.1 YES 86.5% 96.1% +9.6pp SDXL YES 93.5% 99.0% +5.5pp SD 3 YES 85.4% 97.5% +12.1pp Midjourney v6 Unknown 88.5% 99.5% +11.0pp DALL-E 3 NO 98.7% 98.2% -0.5pp CLIP advantage on CLIP generators: +9.1pp average CLIP advantage on non-CLIP generators: -0.5pp (hand-crafted wins) Replaces per-experiment PDFs with single consolidated research report (negate_research_report.pdf) covering all experiments, scaling analysis, CLIP bias findings, and recommended next steps.
+ 156 handcrafted features (was 49) + 768 frozen ConvNeXt-Tiny + fine-tuned ConvNeXt anime veto model + 3-model ensemble (LightGBM + SVM + RF) with calibrated 3-class output + pause/resume feature extraction cache system ~ feature_artwork.py expanded with Gabor, wavelets, fractal, JPEG ghost, mid-band frequency, patch consistency, linework analysis - removed dead-end test scripts and outdated results from PRs #51/#52
…/wfproc/negate into wfproc-feature/hybrid-artwork-detection
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code migrated in individual modules by necessity to #58, too many merge issues |
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