Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Threat Model & Background II. Threat Model & Background III. Webcam Peeking through Glasses III. Webcam Peeking through Glasses IV. Reflection Recognizability & Factors IV. Reflection Recognizability & Factors V. Cyberspace Textual Target Susceptibility V. Cyberspace Textual Target Susceptibility VI. Website Recognition VI. Website Recognition VII. Discussion VII. Discussion VIII. Related Work VIII. Related Work IX. Conclusion, Acknowledgment, and References IX. Conclusion, Acknowledgment, and References APPENDIX A: Equipment Information APPENDIX A: Equipment Information APPENDIX B: Viewing Angle Model APPENDIX B: Viewing Angle Model APPENDIX C: Video Conferencing Platform Behaviors APPENDIX C: Video Conferencing Platform Behaviors APPENDIX D: Distortion Analysis APPENDIX D: Distortion Analysis APPENDIX E: Web Textual Targets APPENDIX E: Web Textual Targets Screen Peeking Using Cameras. Screen-peeking with cameras through optical emanation reflections has been explored in previous works. In 2008, Backes et al. [26] showed that adversaries can use off-the-shelf telescopes and DSLR cameras to spy victims’ LCD monitor screen contents from up to 30m away by utilizing the reflective objects that can be commonly found next to the monitor screen such as teapots placed on a desk. In 2009, the authors [25] took the attack to the next level by addressing the challenges of motion blur and out-offocus blur by performing deconvolution on the photos with Point Spread Functions (PSF). Our work differs from these previous works by exploiting the victims’ own webcams in video conferences for a remote attack. Such changes call for different imaging enhancing techniques due to the different types of image distortions. In addition, reflective objects on the desks and human eyes cannot be easily utilized due to very large curvatures. We thus exploit the glasses people wear to video conferences as a modern attack vector. [57] proposed a relevant idea of using adversary-controlled webcams to detect changes in webpage links’ colors for inferring visited websites. It requires the adversary to take control over the victim’s webcam with malicious web modules and exploits coarsegrain color variations, while our work studies more natural attack vectors in video conferencing and investigate the limits of textual reconstruction. Screen Peeking Using Cameras. Screen Content Reconstruction With Other Emanations. Besides the direct optical emanations from the screen that we exploit in this work, previous works also explored other channels such as electromagnetic radiation [44], [45], [55] and acoustic emanations [37]. Reconstructing screen contents with such emanations usually requires using additional eavesdropping hardware that is placed close to the victims by the adversary. On the other hand, our work exploits the victim’s own webcams, making the attack more accessible. Screen Content Reconstruction With Other Emanations. Remote Eavesdropping Via Audio/Video Calls. Similar to our work, such attacks assume the adversary and victim are both participants of an audio/video conference, and the adversary can eavesdrop on privacy-sensitive information by analyzing the audio/video channels. For example, Voice-overIP attacks for keystroke inference eavesdrop on the victim’s keyboard inputs by utilizing timing and/or spectrum information embedded in the keystroke acoustic emanations [29], [30], [35], [54]. Recently, Sabra et al. [51] proposed works solving the problem of inferring keystrokes by analyzing the dynamic body movements embedded in the videos during a video call. Hilgefort et al. [39] spies victims’ nearby objects through virtual backgrounds in video calls by carrying out foreground-background analysis and accumulating background pixels. In contrast, our work explores the related problem of content reconstruction using only the optical reflections from participants’ glasses embedded in the videos. Remote Eavesdropping Via Audio/Video Calls. IX. CONCLUSION In this work, we characterized the threat model of the webcam peeking attack in video conferencing settings. We developed mathematical models that describe the relationship between the attack limits and different user-dependent factors. The analysis enables the prediction of future threats as webcam technology evolves. We conducted experiments both in controlled lab settings and with a user study. Results showed that present-day 720p cameras pose threats to the contents on users’ screens when users browse certain big-font websites. Future 4K cameras are predicted to allow adversaries to reconstruct various header texts on popular websites. We also found adversaries can recognize the website users are browsing through webcam peeking with 720 cameras. We analyzed both short-term mitigations and long-term defenses and collected user opinions on the possible protections. ACKNOWLEDGEMENT This work is supported by a gift from Analog Devices Inc. and China NSFC Grant 61925109 and 62201503. We thank our reviewers for their insightful comments that helped us improve this manuscript; we thank Dr. Cheng Yang for volunteering to wear eyeglasses in the lab experiments. REFERENCES [1] Converting diagonal field of view and aspect ratio to horizontal and vertical field of view. http://vrguy.blogspot.com/2013/04/ converting-diagonal-field-of-view-and.html, 2013. [2] Webcam Field of View . https://www.telehealth.org.nz/assets/Uploads/ 1511-webcam-field-of-view.pdf, 2015. [3] Approximate Focal Length for Webcams and Cell Phone Cameras. https: //learnopencv.com/approximate-focal-length-for-webcams-and -cell-phone-cameras/, 2016. 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IEEE transactions on Image Processing, 20(8):2378–2386, 2011. Authors:
(1) Yan Long, Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA (yanlong@umich.edu);
(2) Chen Yan, College of Electrical Engineering, Zhejiang University, Hangzhou, China (yanchen@zju.edu.cn);
(3) Shilin Xiao, College of Electrical Engineering, Zhejiang University, Hangzhou, China (xshilin@zju.edu.cn);
(4) Shivan Prasad, Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA (shprasad@umich.edu);
(5) Wenyuan Xu, College of Electrical Engineering, Zhejiang University, Hangzhou, China (wyxu@zju.edu.cn);
(6) Kevin Fu, Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA (kevinfu@umich.edu). Authors: Authors: (1) Yan Long, Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA (yanlong@umich.edu); (2) Chen Yan, College of Electrical Engineering, Zhejiang University, Hangzhou, China (yanchen@zju.edu.cn); (3) Shilin Xiao, College of Electrical Engineering, Zhejiang University, Hangzhou, China (xshilin@zju.edu.cn); (4) Shivan Prasad, Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA (shprasad@umich.edu); (5) Wenyuan Xu, College of Electrical Engineering, Zhejiang University, Hangzhou, China (wyxu@zju.edu.cn); (6) Kevin Fu, Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA (kevinfu@umich.edu). This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL license. available on arxiv available on arxiv

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