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Recently, productization of face recognition and identification algorithmshave become the most controversial topic about ethical AI. As new policiesaround digital identities are formed, we introduce three face access models ina hypothetical social network, where the user has the power to only appear inphotos they approve. Our approach eclipses current tagging systems and replacesunapproved faces with quantitatively dissimilar deepfakes. In addition, wepropose new metrics specific for this task, where the deepfake is generated atrandom with a guaranteed dissimilarity. We explain access models based onstrictness of the data flow, and discuss impact of each model on privacy,usability, and performance. We evaluate our system on Facial Descriptor Datasetas the real dataset, and two synthetic datasets with random and equal classdistributions. Running seven SOTA face recognizers on our results, MFMC reducesthe 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.

My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization