EgoPrivacy: What Your First-Person Camera Says About You?
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.
6851360c8a68fee7f6ba4c1a
Yijiang Li
6851360c8a68fee7f6ba4c1b
Genpei Zhang
6851360c8a68fee7f6ba4c1c
Jiacheng Cheng
6851360c8a68fee7f6ba4c1d
Yi Li
6851360c8a68fee7f6ba4c1e
Xiaojun Shan
6851360c8a68fee7f6ba4c1f
Dashan Gao
6851360c8a68fee7f6ba4c20
Jiancheng Lyu
6851360c8a68fee7f6ba4c21
Yuan Li
6851360c8a68fee7f6ba4c22
Ning Bi
6851360c8a68fee7f6ba4c23
Nuno Vasconcelos
EgoPrivacy: What Your First-Person Camera Says About You?
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.
EgoPrivacy evaluates privacy risks in egocentric vision through a large-scale benchmark, revealing that foundation models can infer private information about camera wearers with high accuracy in zero-shot settings.
EgoPrivacy egocentric vision privacy threats ego-to-exo retrieval demographic privacy exocentric videos foundation models zero-shot settings