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

arXiv: https://arxiv.org/abs/2204.11853 · HTML: https://mlsanity.com/paper/2204.11853/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis](https://mlsanity.com/paper/2007.12909/) (2020)
- [A Survey of Machine Learning Techniques in Adversarial Image Forensics](https://mlsanity.com/paper/2010.09680/) (2020)
- [On the use of Benford's law to detect GAN-generated images](https://mlsanity.com/paper/2004.07682/) (2020)
- [CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing](https://mlsanity.com/paper/1805.11318/) (2018)
- [Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System](https://mlsanity.com/paper/2106.15130/) (2021)
- [FOCAL: A Forgery Localization Framework based on Video Coding Self-Consistency](https://mlsanity.com/paper/2008.10454/) (2020)
- [The privacy protection effectiveness of the video conference platforms' virtual background and the privacy concerns from the end-users](https://mlsanity.com/paper/2110.12493/) (2021)

## Abstract (arXiv)

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%.