Search Results for author:
Found 8 papers, 5 papers with code
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation of Not Safe for Work (NSFW) content.
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model
We propose a simple yet effective adversarially robust anomaly detection method, \textit{AdvRAD}, that allows the diffusion model to act both as an anomaly detector and adversarial purifier.
Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization
Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications.
WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response
The recent breakthrough in large language models (LLMs) such as ChatGPT has revolutionized production processes at an unprecedented pace.
Federated Learning with Projected Trajectory Regularization
Specifically, FedPTR allows local clients or the server to optimize an auxiliary (synthetic) dataset that mimics the learning dynamics of the recent model update and utilizes it to project the next-step model trajectory for local training regularization.
Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
In this work, we show that it is possible to conduct stealthy and persistent unalignment on large language models via backdoor injections.
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
In this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks.
RLCard: A Toolkit for Reinforcement Learning in Card Games
The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
