Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities.

However, existing MU evaluation systems present several key challenges that can result in incomplete and inaccurate assessments.

Earlier research highlighted DMs' vulnerability todatapoisoning attacks, butthese studies placed stricter requirements than conventional methods like'BadNets' inimage classification.

Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising.

Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction.

The techniques of machine unlearning, also known as concept erasing, have been developed to address these risks.