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. In this paper, we propose an algorithm based on well-known warping techniques (common for animation and artistic purposes) to obfuscate faces in video surveillance, aiming to overcome these shortcomings. To demonstrate the feasibility of such an approach, we apply warping algorithm to faces in a standard Yale dataset and run face detection and recognition algorithms on the resulted images. Experiments demonstrate the tradeoff between warping strength and accuracy for both detection and recognition.