Privacy-Aware Camera 2.0 Technical Report

TL;DR

This paper introduces a privacy-preserving perception framework that transforms raw images into abstract features at the edge, ensuring irreversibility and balancing privacy with semantic understanding in sensitive environments.

Contribution

It proposes a novel AI-driven edge-cloud architecture using nonlinear mapping and stochastic noise to protect privacy while enabling behavior recognition and visual reconstruction.

Findings

Raw images are transformed into irreconstructable feature vectors.

The framework balances privacy preservation with effective behavior recognition.

It enables visual reconstruction without exposing raw image data.

Abstract

With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound privacy-security paradox. Existing privacy-preserving approaches, including physical desensitization, encryption, and obfuscation, often compromise semantic understanding or fail to ensure mathematically provable irreversibility. Although Privacy Camera 1.0 eliminated visual data at the source to prevent leakage, it provided only textual judgments, leading to evidentiary blind spots in disputes. To address these limitations, this paper proposes a novel privacy-preserving perception framework based on the AI Flow paradigm and a collaborative edge-cloud architecture. By deploying a visual desensitizer at the edge, raw images are transformed in real time into abstract feature vectors through nonlinear mapping and stochastic noise injection under the Information Bottleneck principle, ensuring identity-sensitive information is stripped and original images are mathematically unreconstructable. The abstract representations are transmitted to the cloud for behavior recognition and semantic reconstruction via a "dynamic contour" visual language, achieving a critical balance between perception and privacy while enabling illustrative visual reference without exposing raw images.