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Turn Your Face Into An Attack Surface: Screen Attack Using Facial Reflections in Video Conferencing

arXiv Security Archived Apr 09, 2026 ✓ Full text saved

arXiv:2604.06729v1 Announce Type: new Abstract: In video conferencing, human faces serve as the primary visual focal points, playing multifaceted roles that enhance visual communication and emotional connection. However, we argue that a human face is also a side channel, which can unwittingly leak on-screen information through online video feeds. To demonstrate this, we conduct feasibility studies, which reveal that, illuminated by both ambient light and light emitted from displays, the human fa

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    Computer Science > Cryptography and Security [Submitted on 8 Apr 2026] Turn Your Face Into An Attack Surface: Screen Attack Using Facial Reflections in Video Conferencing Yong Huang, Yanzhao Lu, Mingyang Chen, En Zhang, Jiazi Li, Wanqing Tu In video conferencing, human faces serve as the primary visual focal points, playing multifaceted roles that enhance visual communication and emotional connection. However, we argue that a human face is also a side channel, which can unwittingly leak on-screen information through online video feeds. To demonstrate this, we conduct feasibility studies, which reveal that, illuminated by both ambient light and light emitted from displays, the human face can reflect optical variations of different on-screen content. The paper then proposes FaceTell, a novel side-channel attack system that eavesdrops on fine-grained application activities from pervasive yet subtle facial reflections during video conferencing. We implement FaceTell in a real-world testbed with three different brands of laptops and four mainstream video conferencing platforms. FaceTell is then evaluated with 24 human subjects across 13 unique indoor environments. With more than 12 hours of video data, FaceTell achieves a high accuracy of 99.32% for eavesdropping on 28 popular applications and is resilient to many practical impact factors. Finally, potential countermeasures are proposed to mitigate this new attack. Comments: To appear in USENIX Security 2026 Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.06729 [cs.CR]   (or arXiv:2604.06729v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.06729 Focus to learn more Submission history From: Yong Huang [view email] [v1] Wed, 8 Apr 2026 06:51:14 UTC (4,423 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Apr 09, 2026
    Archived
    Apr 09, 2026
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