  The heightened emphasis on the regulation of deep generative models,
propelled by escalating concerns pertaining to privacy and compliance with
regulatory frameworks, underscores the imperative need for precise control
mechanisms over these models. This urgency is particularly underscored by
instances in which generative models generate outputs that encompass
objectionable, offensive, or potentially injurious content. In response,
machine unlearning has emerged to selectively forget specific knowledge or
remove the influence of undesirable data subsets from pre-trained models.
However, modern machine unlearning approaches typically assume access to model
parameters and architectural details during unlearning, which is not always
feasible. In multitude of downstream tasks, these models function as black-box
systems, with inaccessible pre-trained parameters, architectures, and training
data. In such scenarios, the possibility of filtering undesired outputs becomes
a practical alternative. The primary goal of this study is twofold: first, to
elucidate the relationship between filtering and unlearning processes, and
second, to formulate a methodology aimed at mitigating the display of
undesirable outputs generated from models characterized as black-box systems.
Theoretical analysis in this study demonstrates that, in the context of
black-box models, filtering can be seen as a form of weak unlearning. Our
proposed \textbf{\textit{Feature Aware Similarity Thresholding(FAST)}} method
effectively suppresses undesired outputs by systematically encoding the
representation of unwanted features in the latent space.
