  The accelerated use of digital cameras prompts an increasing concern about
privacy and security, particularly in applications such as action recognition.
In this paper, we propose an optimizing framework to provide robust visual
privacy protection along the human action recognition pipeline. Our framework
parameterizes the camera lens to successfully degrade the quality of the videos
to inhibit privacy attributes and protect against adversarial attacks while
maintaining relevant features for activity recognition. We validate our
approach with extensive simulations and hardware experiments.
