With the widespread application of computer vision, the scenarios in terms of visual privacy have become increasingly diverse and meanwhile numerous studies have been conducted to address privacy concerns in these scenarios. However, these studies are individually tailored for specific scenarios, making their layouts challenging to be drawn upon easily. When encountering a new scenario, it takes significant additional efforts to redesign a scheme due to the low referability of previous works. To tackle this issue, we explore commonalities among existing works and propose a generalized framework to meet the demand for visual privacy protection in various scenarios. Our framework is elaborately organized into several crucial steps, including privacy definition, scenario abstraction, algorithm design, and effect evaluation. It serves as a guide for researchers to efficiently design visual privacy protection schemes. In our framework, we establish a unified standard for quantifying privacy and introduce a novel constrained optimization theory to balance privacy and usability, which contributes to a broader understanding of visual privacy protection. Furthermore, we present an instance under the guidance of the framework that can support identity protection and attribute control scenarios through a diffusion-based model. Extensive experimental results demonstrate the effectiveness of our framework.

中文翻译：

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了解视觉隐私保护：以面部隐私为例的通用框架

随着计算机视觉的广泛应用，视觉隐私方面的场景变得越来越多样化，同时也有大量的研究来解决这些场景中的隐私问题。然而，这些研究都是针对特定场景单独定制的，使得它们的布局难以轻松绘制。当遇到新的场景时，由于之前的工作可参考性较低，需要花费大量的额外精力来重新设计方案。为了解决这个问题，我们探索了现有作品的共性，并提出了一个通用框架来满足各种场景下视觉隐私保护的需求。我们的框架精心组织为几个关键步骤，包括隐私定义、场景抽象、算法设计和效果评估。为研究人员高效设计视觉隐私保护方案提供指导。在我们的框架中，我们建立了量化隐私的统一标准，并引入了一种新颖的约束优化理论来平衡隐私和可用性，这有助于更广泛地理解视觉隐私保护。此外，我们在框架的指导下提出了一个实例，可以通过基于扩散的模型支持身份保护和属性控制场景。大量的实验结果证明了我们框架的有效性。