PRIVATEDIT：面向面部生成图像编辑的隐私保护流程
PRIVATEEDIT: A Privacy-Preserving Pipeline for Face-Centric Generative Image Editing
AI总结：
PRIVATEDIT提出了一种隐私保护的面部生成图像编辑流程，通过设备端分割和遮罩技术实现用户对生物特征数据的控制，无需修改第三方模型，确保隐私安全。
摘要：
近年来，生成图像编辑技术的进步使各种应用成为可能，从专业人像生成到虚拟形象风格化。然而，这些系统通常需要将高质量面部图像上传到第三方模型，这引发了对生物特征隐私、数据滥用和用户同意的担忧。我们提出了一种隐私保护流程，该流程在支持高质量编辑的同时，使用户在面部使用场景中对生物特征数据保持控制。我们的方法利用设备上的分割和遮罩技术，将身份敏感区域与可编辑图像上下文分离，从而在不修改第三方生成模型的情况下实现安全、受用户控制的编辑。与传统的基于云的工具不同，PRIVATEDIT 默认强制隐私：生物特征数据从不暴露或传输。这种设计不需要访问或重新训练第三方模型，使其能够与广泛的商业API兼容。通过将隐私作为核心设计约束，我们的系统支持负责任的生成AI，以用户自主和信任为中心。该流程包括一个可调节的遮罩机制，使用户能够控制隐藏多少面部信息，从而根据信任水平或使用场景平衡隐私和输出保真度。我们展示了其在专业和创意工作流程中的适用性，并提供了一个用于选择性匿名化的用户界面。通过倡导生成AI中的隐私设计，我们的工作既提供了技术可行性，也提供了保护数字身份的规范指导。源代码可在https://github.com/Dipeshtamboli/PrivateEdit-Privacy-Preserving-GenAI上获取。
英文摘要：
Recent advances in generative image editing have enabled transformative applications, from professional head shot generation to avatar stylization. However, these systems often require uploading high-fidelity facial images to third-party models, raising concerns around biometric privacy, data misuse, and user consent. We propose a privacy-preserving pipeline that supports high-quality editing while keeping users in control over their biometric data in face-centric use cases. Our approach separates identity-sensitive regions from editable image context using on-device segmentation and masking, enabling secure, user-controlled editing without modifying third-party generative models. Unlike traditional cloud-based tools, PRIVATEEDIT enforces privacy by default: biometric data is never exposed or transmitted. This design requires no access to or retraining of third-party models, making it compatible with a wide range of commercial APIs. By treating privacy as a core design constraint, our system supports responsible generative AI centered on user autonomy and trust. The pipeline includes a tunable masking mechanism that lets users control how much facial information is concealed, allowing them to balance privacy and output fidelity based on trust level or use case. We demonstrate its applicability in professional and creative workflows and provide a user interface for selective anonymization. By advocating privacy-by-design in generative AI, our work offers both technical feasibility and normative guidance for protecting digital identity. The source code is available at https://github.com/Dipeshtamboli/PrivateEdit-Privacy-Preserving-GenAI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
