  Visual diffusion models have revolutionized the field of creative AI,
producing high-quality and diverse content. However, they inevitably memorize
training images or videos, subsequently replicating their concepts, content, or
styles during inference. This phenomenon raises significant concerns about
privacy, security, and copyright within generated outputs. In this survey, we
provide the first comprehensive review of replication in visual diffusion
models, marking a novel contribution to the field by systematically
categorizing the existing studies into unveiling, understanding, and mitigating
this phenomenon. Specifically, unveiling mainly refers to the methods used to
detect replication instances. Understanding involves analyzing the underlying
mechanisms and factors that contribute to this phenomenon. Mitigation focuses
on developing strategies to reduce or eliminate replication. Beyond these
aspects, we also review papers focusing on its real-world influence. For
instance, in the context of healthcare, replication is critically worrying due
to privacy concerns related to patient data. Finally, the paper concludes with
a discussion of the ongoing challenges, such as the difficulty in detecting and
benchmarking replication, and outlines future directions including the
development of more robust mitigation techniques. By synthesizing insights from
diverse studies, this paper aims to equip researchers and practitioners with a
deeper understanding at the intersection between AI technology and social good.
We release this project at
https://github.com/WangWenhao0716/Awesome-Diffusion-Replication.
