Real or Virtual: A Video Conferencing Background Manipulation-Detection System
Ehsan Nowroozi, Yassine Mekdad, Mauro Conti, Simone Milani, Selcuk, Uluagac, Berrin Yanikoglu

TL;DR
This paper introduces a robust detection system to distinguish real from virtual backgrounds in video conferencing, achieving high accuracy and addressing adversarial attacks with a newly created dataset.
Contribution
The paper presents a novel background authenticity detector for video conferencing that is robust against adversarial attacks and introduces a publicly available dataset.
Findings
Achieves 99.80% accuracy in real vs. virtual background detection.
Robust against adversarial attacks including geometric transformations.
Effective using CRSPAM1372 features and post-processing techniques.
Abstract
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…
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Taxonomy
TopicsDigital Media Forensic Detection · Anomaly Detection Techniques and Applications · Adversarial Robustness in Machine Learning
MethodsAttentive Walk-Aggregating Graph Neural Network
