Imagine a secret agent remotely looking around in a classified facility, using a virtual reality
headset. If security measures fail and an untrustworthy employee distributes this media, the
agency wants to identify the source of the leak. Therefore, it should send a personalized version of
the video to each user, who is identified by a watermark hidden in his or her version. When
someone illegally redistributes such a version, the agency extracts the watermark in order to
identify the leaker. Existing watermarking schemes face problems of scalability, imperceptibility or
robustness and do not focus on 360-degree video yet.
As a solution, I will develop a novel watermarking approach based on new, personalized streaming
technologies. In these technologies, content providers deliver a personalized experience to the
users by adapting to their network conditions and position of the viewport in a 360-degree video.
In my master’s thesis, I laid the foundation for a watermarking scheme using a video encoder
made for personalized streaming. In this research project, I will improve this technique and adapt
it to 360-degree video. Additionally, I will expand the concept such that it skips an explicit
watermark embedding step entirely. Instead, it will identify the user based on the way in which the
media was personalized. In conclusion, I will enable media providers to simultaneously deliver a
personalized experience and protect their content when traditional security measures fail.