EgoPrivacy: What Your First-Person Camera Says About You?

TL;DR

EgoPrivacy introduces a large-scale benchmark and novel attack strategies to evaluate and demonstrate the significant privacy risks posed by first-person videos, revealing that private information about the wearer can be highly inferred.

Contribution

This work presents EgoPrivacy, the first comprehensive benchmark for egocentric privacy risks, and proposes Retrieval-Augmented Attack to enhance privacy breach effectiveness.

Findings

Foundation models can recover private attributes with 70-80% accuracy.

Private information like identity and demographics is highly susceptible to inference.

EgoPrivacy provides a systematic evaluation of privacy threats in first-person videos.

Abstract

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.