  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. However, the more powerful the DMs, the more harmful
they potentially are. Recent studies have shown that DMs are prone to a wide
range of attacks, including adversarial attacks, membership inference, backdoor
injection, and various multi-modal threats. Since numerous pre-trained DMs are
published widely on the Internet, potential threats from these attacks are
especially detrimental to the society, making DM-related security a worth
investigating topic. Therefore, in this paper, we conduct a comprehensive
survey on the security aspect of DMs, focusing on various attack and defense
methods for DMs. First, we present crucial knowledge of DMs with five main
types of DMs, including denoising diffusion probabilistic models, denoising
diffusion implicit models, noise conditioned score networks, stochastic
differential equations, and multi-modal conditional DMs. We further survey a
variety of recent studies investigating different types of attacks that exploit
the vulnerabilities of DMs. Then, we thoroughly review potential
countermeasures to mitigate each of the presented threats. Finally, we discuss
open challenges of DM-related security and envision certain research directions
for this topic.
