Recently, Machine Unlearning (MU) has gained considerable attention for its potential to
improve AI safety by removing the influence of specific data from trained Machine Learning …

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 …

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

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 …

The rapid expansion of large-scale text-to-image diffusion models has raised growing
concerns regarding their potential misuse in creating harmful or misleading content. In this …

Generative AI technologies have been deployed in many places, such as (multimodal) large
language models and vision generative models. Their remarkable performance should be …

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 models (DMs) have achieved state-of-the-art performance on various generative
tasks such as image synthesis, text-to-image, and text-guided image-to-image generation …

Recent advancements in Text-to-Image (T2I) models have raised significant safety concerns
about their potential misuse for generating inappropriate or Not-Safe-For-Work (NSFW) …