SPRINT achieves over 99% attribution accuracy on FFHQ images across multiple model pools while reducing adaptive attack success rates to 1% or below by keeping verification targets secret.
Improved consistency regularization for gans,
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.
citing papers explorer
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SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction
SPRINT achieves over 99% attribution accuracy on FFHQ images across multiple model pools while reducing adaptive attack success rates to 1% or below by keeping verification targets secret.
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A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence
A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.