Video calls have become an essential part of today's business life, especially due to the Corona pandemic. Several industry branches enable their employees to work from home and collaborate via video conferencing services. While remote work offers benefits for health safety and personal mobility, it also poses privacy risks. Visual content is directly transmitted from the private living environment of employees to third parties, potentially exposing sensitive information. To counter this threat, video conferencing services support replacing the visible environment of a video call with a virtual background. This replacement, however, is imperfect, leaking tiny regions of the real background in video frames. In this paper, we explore how these leaks in virtual backgrounds can be exploited to reconstruct regions of the real environment. To this end, we build on recent techniques of computer vision and derive an approach capable of extracting and aggregating leaked pixels in a video call. In an empirical study with the services Zoom, Webex, and Google Meet, we can demonstrate that the exposed fragments of the reconstructed background are sufficient to spot different objects. From 114 video calls with virtual backgrounds, 35% enable to correctly identify objects in the environment. We conclude that virtual backgrounds provide only limited protection, and alternative defenses are needed.