  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.
