EgoPrivacy: What Your First-Person Camera Says About You?
Authors: name: Yijiang Li, name: Genpei Zhang, name: Jiacheng Cheng, name: Yi Li, name: Xiaojun Shan, name: Dashan Gao, name: Jiancheng Lyu, name: Yuan Li, name: Ning Bi, name: Nuno Vasconcelos

While the rapid proliferation of wearable cameras has raised significant
concerns about egocentric video privacy, prior work has largely overlooked the
unique privacy threats posed to the camera wearer. This work investigates the
core question: How much privacy information about the camera wearer can be
inferred from their first-person view videos? We introduce EgoPrivacy, the
first large-scale benchmark for the comprehensive evaluation of privacy risks
in egocentric vision. EgoPrivacy covers three types of privacy (demographic,
individual, and situational), defining seven tasks that aim to recover private
information ranging from fine-grained (e.g., wearer's identity) to
coarse-grained (e.g., age group). To further emphasize the privacy threats
inherent to egocentric vision, we propose Retrieval-Augmented Attack, a novel
attack strategy that leverages ego-to-exo retrieval from an external pool of
exocentric videos to boost the effectiveness of demographic privacy attacks. An
extensive comparison of the different attacks possible under all threat models
is presented, showing that private information of the wearer is highly
susceptible to leakage. For instance, our findings indicate that foundation
models can effectively compromise wearer privacy even in zero-shot settings by
recovering attributes such as identity, scene, gender, and race with 70-80%
accuracy. Our code and data are available at
https://github.com/williamium3000/ego-privacy.