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.
Flexible and secure watermarking for latent diffusion model
4 Pith papers cite this work. Polarity classification is still indexing.
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ClusterMark applies visual token clustering to create robust in-generation watermarks for autoregressive image models, improving detectability under perturbations compared to direct token biasing while preserving quality.
PREFAB applies preference learning grounded in the peak-end rule to let users annotate only key affective change segments while interpolating the rest, reducing workload and improving confidence in a 25-participant study.
Tiny NeRV models using capacity scaling, frequency-aware distillation, and low-precision quantization achieve favorable quality-efficiency trade-offs with far fewer parameters and lower computational costs than standard NeRV.
citing papers explorer
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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.
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ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token Clustering
ClusterMark applies visual token clustering to create robust in-generation watermarks for autoregressive image models, improving detectability under perturbations compared to direct token biasing while preserving quality.
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PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation
PREFAB applies preference learning grounded in the peak-end rule to let users annotate only key affective change segments while interpolating the rest, reducing workload and improving confidence in a 25-participant study.
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TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference
Tiny NeRV models using capacity scaling, frequency-aware distillation, and low-precision quantization achieve favorable quality-efficiency trade-offs with far fewer parameters and lower computational costs than standard NeRV.