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.

中文翻译：

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不要用虚拟背景欺骗您的雇主：视频会议操纵检测系统

由于隐私问题，上一代视频会议软件允许用户利用虚拟背景来隐藏他们的个人环境，尤其是在与其他雇主的正式会议上。另一方面，用户可能想通过考虑虚拟背景来隐藏他们所在的位置来欺骗会议中的人们。在这种情况下，开发工具以了解用于在会议中愚弄人们的虚拟背景发挥了重要作用。此外，此类检测器必须证明能够抵御不同类型的攻击，因为恶意用户可以通过对视频应用一组对抗性编辑步骤来隐藏任何暴露的足迹来欺骗检测器。在本文中，我们研究了一种检测视频会议用户背景是否真实的有效工具的可行性。特别是，我们提供了第一个计算像素共生矩阵并使用它们来搜索光谱和空间波段之间的不一致的工具。我们的实验证实，交叉共现矩阵提高了检测器对不同类型攻击的鲁棒性。这项工作在彩色垃圾邮件功能方面的表现尤其值得注意。此外，性能在鲁棒性与后处理（如几何变换、过滤、对比度增强和具有不同质量因素的 JPEG 压缩）方面尤为重要。在彩色垃圾邮件功能方面的表现尤其值得注意。此外，性能在鲁棒性与后处理（如几何变换、过滤、对比度增强和具有不同质量因素的 JPEG 压缩）方面尤为重要。在彩色垃圾邮件功能方面的表现尤其值得注意。此外，性能在鲁棒性与后处理（如几何变换、过滤、对比度增强和具有不同质量因素的 JPEG 压缩）方面尤为重要。