Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

Recently, the text-to-image diffusion model has gained considerable attention from the
community due to its exceptional image generation capability. A representative model …

We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …

Text-to-image (T2I) models can be maliciously used to generate harmful content such as
sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. Previous …

In recent years Text-to-Image (T2I) models have seen remarkable advancements gaining
widespread adoption. However this progress has inadvertently opened avenues for …

The trustworthy machine learning (ML) community is increasingly recognizing the crucial
need for models capable of selectively 'unlearning'data points after training. This leads to the …

Diffusion-based models have gained significant popularity for text-to-image generation due
to their exceptional image-generation capabilities. A risk with these models is the potential …

Abstract Concept erasure in text-to-image diffusion models aims to disable pre-trained
diffusion models from generating images related to a target concept. To perform reliable …

Text-to-image models encounter safety issues, including concerns related to copyright and
Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for …