CSGuard binds diffusion-model watermarks to a secret matrix via compressed sensing, cutting forgery attack success from 100% to 28.12% while preserving 100% detection on legitimate images.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
PG-3DGS couples 3D Gaussian Splatting with differentiable physics so that optimized shapes satisfy both visual fidelity and physical objectives such as pouring and aerodynamic lift, with real-world 3D-printed validation.
Generative models learn rules before memorizing data, creating an innovation window whose width depends on dataset size and rule complexity, observed in both diffusion and autoregressive architectures.
PECKER uses a saliency mask to prioritize parameter updates in distillation-based unlearning, achieving shorter training times for class and concept forgetting on CIFAR-10 and STL-10 while matching prior methods' efficacy.
MIRAGE combines a medical CLIP model, a diffusion generator, and an LLM into an accessible interface for retrieving and creating educational medical images and texts.
citing papers explorer
-
CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint
CSGuard binds diffusion-model watermarks to a secret matrix via compressed sensing, cutting forgery attack success from 100% to 28.12% while preserving 100% detection on legitimate images.
-
PG-3DGS: Optimizing 3D Gaussian Splatting to Satisfy Physics Objectives
PG-3DGS couples 3D Gaussian Splatting with differentiable physics so that optimized shapes satisfy both visual fidelity and physical objectives such as pouring and aerodynamic lift, with real-world 3D-printed validation.
-
The two clocks and the innovation window: When and how generative models learn rules
Generative models learn rules before memorizing data, creating an innovation window whose width depends on dataset size and rule complexity, observed in both diffusion and autoregressive architectures.
-
PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
PECKER uses a saliency mask to prioritize parameter updates in distillation-based unlearning, achieving shorter training times for class and concept forgetting on CIFAR-10 and STL-10 while matching prior methods' efficacy.
-
MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education
MIRAGE combines a medical CLIP model, a diffusion generator, and an LLM into an accessible interface for retrieving and creating educational medical images and texts.