# Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System

arXiv: https://arxiv.org/abs/2106.15130 · HTML: https://mlsanity.com/paper/2106.15130/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [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)
- [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)
- [CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing](https://mlsanity.com/paper/1805.11318/) (2018)

## Built on by (most cited first, 3 of 3)

- [GRACE: Loss-Resilient Real-Time Video through Neural Codecs](https://mlsanity.com/paper/2305.12333/) (2023)
- [Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents](https://mlsanity.com/paper/2304.02982/) (2023)
- [Real or Virtual: A Video Conferencing Background Manipulation-Detection System](https://mlsanity.com/paper/2204.11853/) (2022)

## Abstract (arXiv)

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.