用智能噪声匿名化事件数据，防人防AI识别，保护隐私。
AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy
- 引入可学习的、数据相关的噪声，隐藏事件数据中的人脸信息
- 使攻击者重识别能力降低60%，同时保留下游任务可用信息
- 对未见数据泛化好，抵御图像重建和逆向攻击，适合安防场景
深度神经网络在重识别方面的进步，加之近年来公共监控的普及，严重威胁个人隐私。事件相机曾被视为潜在解决方案，因其输出稀疏，人类难以理解。但近期深度学习进展表明，神经网络能利用事件数据重建高质量灰度图像并实现个体重识别。本文首次提出事件数据匿名化流程，不仅防范人工重识别，更有效抵御神经网络攻击。方法通过引入可学习的数据相关噪声，覆盖原始事件数据中的敏感信息，使攻击者重识别能力下降最高达60%，同时保持足够信息以支持下游任务。该匿名化方案在未见数据上表现良好，且对图像重建与逆向攻击具有鲁棒性。代码已开源：https://github.com/dfki-av/AnonyNoise。
原文摘要 · Abstract (English)
The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual privacy. Event cameras were initially considered as a promising solution since their output is sparse and therefore difficult for humans to interpret. However, recent advances in deep learning proof that neural networks are able to reconstruct high-quality grayscale images and re-identify individuals using data from event cameras. In our paper, we contribute a crucial ethical discussion on data privacy and present the first event anonymization pipeline to prevent re-identification not only by humans but also by neural networks. Our method effectively introduces learnable data-dependent noise to cover personally identifiable information in raw event data, reducing attackers' re-identification capabilities by up to 60%, while maintaining substantial information for the performing of downstream tasks. Moreover, our anonymization generalizes well on unseen data and is robust against image reconstruction and inversion attacks. Code: https://github.com/dfki-av/AnonyNoise
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