Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding

The introduction of the Anonymizing Adapter Module (AAM) marks a significant advancement in privacy preservation for video understanding. Traditional methods focus on pixel-level anonymization, which can compromise the utility of video foundation models and require extensive retraining. In contrast, AAM operates in the latent space, allowing for a plug-and-play application to frozen video encoders, thus minimizing computational demands. This innovative approach not only reduces privacy leakage by 35% but also maintains near-baseline performance across various downstream tasks. As video content becomes increasingly prevalent in digital spaces, ensuring privacy while retaining functionality is essential, making AAM a pivotal development in the field of artificial intelligence.