  Recently, productization of face recognition and identification algorithms
have become the most controversial topic about ethical AI. As new policies
around digital identities are formed, we introduce three face access models in
a hypothetical social network, where the user has the power to only appear in
photos they approve. Our approach eclipses current tagging systems and replaces
unapproved faces with quantitatively dissimilar deepfakes. In addition, we
propose new metrics specific for this task, where the deepfake is generated at
random with a guaranteed dissimilarity. We explain access models based on
strictness of the data flow, and discuss impact of each model on privacy,
usability, and performance. We evaluate our system on Facial Descriptor Dataset
as the real dataset, and two synthetic datasets with random and equal class
distributions. Running seven SOTA face recognizers on our results, MFMC reduces
the average accuracy by 61%. Lastly, we extensively analyze similarity metrics,
deepfake generators, and datasets in structural, visual, and generative spaces;
supporting the design choices and verifying the quality.
