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English(EN) Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

新AI方法增强自动驾驶汽车数据集中的行人隐私

作者 PulseAugur 编辑部 · [4 个来源] ·

研究人员开发了保护自动驾驶汽车数据集中的行人隐私的新方法。其中一种方法在一个五阶段的管道中详细介绍，使用 Roop 等换脸模型来隐藏身份，同时保留用于训练AI模型的重要面部特征。另一种方法 LPID，在图像中引入不易察觉的扰动，即使在提取和调整人脸大小后，也能阻止未经授权的人脸识别模型从中学习。 AI

影响 这些保护隐私的技术可以创建更大、更多样化的自动驾驶汽车训练数据集，而不会损害个人隐私。

排序理由 该集群包含两篇在 arXiv 上发表的研究论文，详细介绍了AI驱动的隐私保护新方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新AI方法增强自动驾驶汽车数据集中的行人隐私

报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Roba H. Farouk, Catherine M. Elias ·

    Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

    arXiv:2607.08402v1 Announce Type: cross Abstract: Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are c…

  2. arXiv cs.AI TIER_1 English(EN) · Catherine M. Elias ·

    Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

    Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical models used in AVs, requiring datasets inv…

  3. arXiv cs.CV TIER_1 English(EN) · Byunghoon Oh, Sunghwan Park, Jaewoo Lee ·

    不可学习的面孔：隐私保护在提取管道中幸存

    arXiv:2607.05996v1 Announce Type: new Abstract: Unlearnable examples keep publicly shared photos from being learned by unauthorized face-recognition models. An imperceptible perturbation, added before sharing, makes any model trained on the protected photos fail on clean faces. T…

  4. arXiv cs.CV TIER_1 English(EN) · Jaewoo Lee ·

    不可学习的人脸：隐私保护在提取管道中幸存

    Unlearnable examples keep publicly shared photos from being learned by unauthorized face-recognition models. An imperceptible perturbation, added before sharing, makes any model trained on the protected photos fail on clean faces. The perturbation is crafted on the shared image, …

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