Search Results for author:
Found 7 papers, 6 papers with code
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
In response, we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring vivid visual representations for digital copyright authentication.
Improving Adversarial Attacks on Latent Diffusion Model
We show that these attacks add an extra error to the score function of adversarial examples predicted by LDM.
Toward effective protection against diffusion based mimicry through score distillation
In this work, we present novel findings on attacking latent diffusion models (LDM) and propose new plug-and-play strategies for more effective protection.
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic.
Mist: Towards Improved Adversarial Examples for Diffusion Models
Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual properties and copyright.
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios.
