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.
Abstract (translated)
随着智能传感技术在如卫生间和更衣室等高度敏感环境中的部署日益增加，视觉监控系统面临着一个深刻的隐私与安全矛盾。现有的隐私保护方法，包括物理去敏感化、加密和混淆处理，在提供语义理解方面往往存在缺陷，或者无法确保数学意义上的不可逆性。尽管Privacy Camera 1.0通过在源头消除视觉数据来防止泄露，但它仅提供了文本判断结果，这在争议中导致了证据盲点的出现。为解决这些限制，本文提出了一种基于AI流范式和协作边缘-云架构的新颖隐私保护感知框架。 该方案通过在边缘部署视觉去敏感化器，在非线性映射和信息瓶颈原则下注入随机噪声，将原始图像实时转换为抽象特征向量。这样确保了身份相关的信息被剥离且原始图像无法从数学上重建。接着，这些抽象表示传输到云端进行行为识别及语义重构，通过“动态轮廓”视觉语言实现感知与隐私之间的关键平衡，同时支持可视化参考而不暴露原始图像。
URL
https://arxiv.org/abs/2603.04775
