My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization
This addresses privacy concerns for social media users by enabling anonymization, though it is an incremental improvement over existing tagging systems.
The paper tackles the problem of unauthorized face recognition in social media by introducing a system that replaces unapproved faces with dissimilar deepfakes, reducing average recognition accuracy by 61% across seven state-of-the-art recognizers.
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.