重新思考肖像涂抹与保护隐私的隐私问题
Sihan Ma1, Jizhizi Li1, Jing Zhang1
1The University of Sydney, Sydney, NSW Australia.
International journal of computer vision
|June 26, 2023
概括
本研究介绍了P3M-10k,这是第一个大规模的匿名数据集,用于保护隐私的肖像涂料 (P3M). 它还介绍了P3M-Net和P3M-CP,改善了保护隐私的模型概括.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 保护隐私的人工智能
背景情况:
- 传统的肖像依赖于可识别的图像,造成隐私风险.
- 现有的方法缺乏强大的隐私保护能力.
- 需要匿名数据集来训练和评估保护隐私的模型.
研究的目的:
- 介绍P3M-10k,这是一个大规模的匿名基准,用于保护隐私的肖像涂料 (P3M).
- 开发和评估一个统一的模型 (P3M-Net) 和一个新的策略 (P3M-CP) 以提高隐私和通用性.
- 解决隐私保护培训 (PPT) 的跨领域绩效差距.
主要方法:
- 创建了P3M-10k数据集:高分辨率的10,421张,面部模糊的肖像与alpha mattes.
- 开发了P3M-Net,这是一个统一的模型,可以适应CNN和变压器架构.
- 实施P3M-CP策略,使用公共名人图像在数据和特征层面重新获得面部背景.
主要成果:
- 在P3M-10k和公共基准上,P3M-Net在最先进的方法上表现出优越性.
- 在保护隐私的培训环境下,P3M-CP显著提高了跨域概括能力.
- 在PPT下系统评估无trimap和基于trimap的法.
结论:
- P3M-10k作为保护隐私的肖像研究的关键基准.
- P3M-Net和P3M-CP为隐私问题提供了有效的解决方案,并且在上进行了概括.
- 这些发现对未来的研究和保护隐私的人工智能的现实应用具有重大意义.
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