A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
Riemannian-geometric fingerprints of generative models,
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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.
Proposes causal fingerprints via causality-decoupling in pre-trained diffusion residual latent space for improved source attribution across GANs and diffusion models.
citing papers explorer
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Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
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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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Causal Fingerprints of AI Generative Models
Proposes causal fingerprints via causality-decoupling in pre-trained diffusion residual latent space for improved source attribution across GANs and diffusion models.