Privacy-Aware Camera 2.0 Technical Report
This work tackles the critical problem of balancing privacy and security in sensitive environments for visual surveillance systems, offering a novel approach to ensure data irreversibility while maintaining semantic understanding.
This paper addresses the privacy-security paradox in visual surveillance by proposing a novel framework that transforms raw images into abstract feature vectors at the edge, ensuring mathematical irreversibility. These abstract representations are then transmitted to the cloud for behavior recognition and semantic reconstruction, providing illustrative visual references without exposing sensitive raw data.
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.