Report: “Researchers Use AI to Highlight Zoom’s Privacy Risks”

From VentureBeat:

It’s relatively easy to extract personal information including face images, age, gender, and names from public screenshots of video meetings, according to Ben-Gurion University researchers. The coauthors of a newly published study say a combination of image processing, text recognition, and forensics enabled them to cross-reference Zoom data with social network data, demonstrating that meeting participants might be subject to risks they aren’t aware of.

Direct to Full Text VentureBeat Article

Direct to Full Text Research Paper: Zooming Into Video Conferencing Privacy and Security Threats (via arXiv)
22 pages; PDF.

Abstract

The COVID-19 pandemic outbreak, with its related social distancing and shelter-in-place measures, has dramatically affected ways in which people communicate with each other, forcing people to find new ways to collaborate, study, celebrate special occasions, and meet with family and friends. One of the most popular solutions that have emerged is the use of video conferencing applications to replace face-to-face meetings with virtual meetings. This resulted in unprecedented growth in the number of video conferencing users. In this study, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted on the Web. We used image processing, text recognition tools, as well as social network analysis to explore our web crawling curated dataset of over 15,700 collage images, and over 142,000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and sometimes even full names. This type of extracted data can vastly and easily jeopardize people’s security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as young children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual.

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