AI content watermarking exhibits detection disparities across languages, cultures, and demographics due to content-dependent signal properties, with benchmarks failing to disaggregate performance and watermarking held to lower fairness standards than generative models.
Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004
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PDF-GS progressively filters distractors in 3D Gaussian Splatting by exploiting the method's self-suppression of inconsistent signals, yielding high-fidelity distractor-free 3D models without changing the base architecture or adding inference cost.
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Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
AI content watermarking exhibits detection disparities across languages, cultures, and demographics due to content-dependent signal properties, with benchmarks failing to disaggregate performance and watermarking held to lower fairness standards than generative models.
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PDF-GS: Progressive Distractor Filtering for Robust 3D Gaussian Splatting
PDF-GS progressively filters distractors in 3D Gaussian Splatting by exploiting the method's self-suppression of inconsistent signals, yielding high-fidelity distractor-free 3D models without changing the base architecture or adding inference cost.