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

Real or Virtual: A Video Conferencing Background Manipulation-Detection System