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Found 7 papers, 3 papers with code
On the Difficulty of Defending Contrastive Learning against Backdoor Attacks
Recent studies have shown that contrastive learning, like supervised learning, is highly vulnerable to backdoor attacks wherein malicious functions are injected into target models, only to be activated by specific triggers.
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.
IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI
IMPRESS is based on the key observation that imperceptible perturbations could lead to a perceptible inconsistency between the original image and the diffusion-reconstructed image, which can be used to devise a new optimization strategy for purifying the image, which may weaken the protection of the original image from unauthorized data usage (e. g., style mimicking, malicious editing).
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
A natural question is "could alignment really prevent those open-sourced large language models from being misused to generate undesired content?''.
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.
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata.
OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning
Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution.
