  The last-generation video conferencing software allows users to utilize a
virtual background to conceal their personal environment due to privacy
concerns, especially in official meetings with other employers. On the other
hand, users maybe want to fool people in the meeting by considering the virtual
background to conceal where they are. In this case, developing tools to
understand the virtual background utilize for fooling people in meeting plays
an important role. Besides, such detectors must prove robust against different
kinds of attacks since a malicious user can fool the detector by applying a set
of adversarial editing steps on the video to conceal any revealing footprint.
In this paper, we study the feasibility of an efficient tool to detect whether
a videoconferencing user background is real. In particular, we provide the
first tool which computes pixel co-occurrences matrices and uses them to search
for inconsistencies among spectral and spatial bands. Our experiments confirm
that cross co-occurrences matrices improve the robustness of the detector
against different kinds of attacks. This work's performance is especially
noteworthy with regard to color SPAM features. Moreover, the performance
especially is significant with regard to robustness versus post-processing,
like geometric transformations, filtering, contrast enhancement, and JPEG
compression with different quality factors.
