During the pandemic the usage of video platforms skyrocketed among office workers and students and even today, when more and more events are held on-site again, the usage of video platforms is at an all-time high. However, the many advantages of these platforms cannot hide some problems. In the professional field, the publication of audio recordings without the consent of the author can get him into trouble. In education, another problem is bullying. The distance from the victim lowers the inhibition threshold for bullying, which means that platforms need tools to combat it. In this work, we present a system, which can not only identify the person leaking the footage, but also identify all other persons present in the footage. This system can be used in both described scenarios.
Digital watermarking technologies are based on the idea of embedding a data-carrying signal in a semi covert manner in a given host image. Here we describe a new approach in which we render the signal itself as an explicit artistic pattern, thereby hiding the signal in plain sight. This pattern may be used as is, or as a texture layer in another image for various applications. There is an immense variety of signal carrying patterns and we present several examples. We also present some results on the detection robustness of these patterns.