无需密钥管理的隐私保护语义分割，支持客户端独立加密
Privacy-Preserving Semantic Segmentation without Key Management
- 每个客户端和图像使用独立密钥加密，全程在密文上训练推理
- 在Cityscapes数据集上使用SETR模型验证，性能损失可控
- 适合医疗影像等高隐私需求场景，避免密钥泄露风险
本文提出一种新型隐私保护语义分割方法，支持每个客户端和图像使用独立密钥。模型创建者与各客户端均使用本地生成的密钥对图像进行加密，模型训练与推理均在加密图像上完成。为缓解性能下降，该方法在模型训练阶段也采用图像加密技术，同时用于测试图像生成。实验在基于视觉变换器的SETR模型下于Cityscapes数据集上验证了该方法的有效性。
原文摘要 · Abstract (English)
This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed method, the model creator and each client encrypt images using locally generated keys, and model training and inference are conducted on the encrypted images. To mitigate performance degradation, an image encryption method is applied to model training in addition to the generation of test images. In experiments, the effectiveness of the proposed method is confirmed on the Cityscapes dataset under the use of a vision transformer-based model, called SETR.
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