  Recently, the popularity and wide use of the last-generation video
conferencing technologies created an exponential growth in its market size.
Such technology allows participants in different geographic regions to have a
virtual face-to-face meeting. Additionally, it enables users to employ a
virtual background to conceal their own environment due to privacy concerns or
to reduce distractions, particularly in professional settings. Nevertheless, in
scenarios where the users should not hide their actual locations, they may
mislead other participants by claiming their virtual background as a real one.
Therefore, it is crucial to develop tools and strategies to detect the
authenticity of the considered virtual background. In this paper, we present a
detection strategy to distinguish between real and virtual video conferencing
user backgrounds. We demonstrate that our detector is robust against two attack
scenarios. The first scenario considers the case where the detector is unaware
about the attacks and inn the second scenario, we make the detector aware of
the adversarial attacks, which we refer to Adversarial Multimedia Forensics
(i.e, the forensically-edited frames are included in the training set). Given
the lack of publicly available dataset of virtual and real backgrounds for
video conferencing, we created our own dataset and made them publicly available
[1]. Then, we demonstrate the robustness of our detector against different
adversarial attacks that the adversary considers. Ultimately, our detector's
performance is significant against the CRSPAM1372 [2] features, and
post-processing operations such as geometric transformations with different
quality factors that the attacker may choose. Moreover, our performance results
shows that we can perfectly identify a real from a virtual background with an
accuracy of 99.80%.
