The widespread use of digital video surveillance systems has also increased the concerns for violation of privacy rights. Since video surveillance systems are invasive, it is a challenge to find an acceptable balance between privacy of the public under surveillance and the functionalities of the systems. Tools for protection of visual privacy available today lack either all or some of the important properties such as security of protected visual data, reversibility (ability to undo privacy protection), simplicity, and independence from the video encoding used. To overcome these shortcomings, in this paper, we propose a morphing-based privacy protection method and focus on its robustness, reversibility, and security properties. We morph faces from a standard FERET dataset and run face detection and recognition algorithms on the resulted images to demonstrate that morphed faces retain the likeness of a face, while making them unrecognizable, which ensures the protection of privacy. Our experiments also demonstrate the influence of morphing strength on robustness and security. We also show how to determine the right parameters of the method.