Penetration Vision through Virtual Reality Headsets: Identifying 360-degree Videos from Head Movements


Anh Nguyen, Xiaokuan Zhang, and Zhisheng Yan, George Mason University


In this paper, we present the first contactless side-channel attack for identifying 360° videos being viewed in a Virtual Reality (VR) Head Mounted Display (HMD). Although the video content is displayed inside the HMD without any external exposure, we observe that user head movements are driven by the video content, which creates a unique side channel that does not exist in traditional 2D videos. By recording the user whose vision is blocked by the HMD via a malicious camera, an attacker can analyze the correlation between the user's head movements and the victim video to infer the video title.

To exploit this new vulnerability, we present INTRUDE, a system for identifying 360° videos from recordings of user head movements. INTRUDE is empowered by an HMD-based head movement estimation scheme to extract a head movement trace from the recording and a video saliency-based trace-fingerprint matching framework to infer the video title. Evaluation results show that INTRUDE achieves over 96% of accuracy for video identification and is robust under different recording environments. Moreover, INTRUDE maintains its effectiveness in the open-world identification scenario.

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