论文标题
选择性操纵分开的表现形式用于隐私感知面部图像处理
Selective manipulation of disentangled representations for privacy-aware facial image processing
论文作者
论文摘要
相机传感器越来越多地与机器学习相结合，以执行各种任务，例如智能监视。由于其计算复杂性，这些机器学习算法中的大多数被卸载到云中进行处理。但是，用户越来越关注第三方云提供商等隐私问题，例如功能蠕变和恶意使用。为了减轻这一点，我们提出了一个基于边缘的过滤阶段，该阶段在将传感器数据传输到云之前，将消除对隐私敏感的属性。我们使用最先进的图像操纵技术，以利用删除的表示形式来实现隐私过滤。我们定义选择加入和退出过滤器操作，并评估其从面部图像过滤私人属性的有效性。此外，我们研究了自然发生的相关性和剩余信息对过滤的影响。我们发现结果有希望，并相信这会进一步研究如何将图像操纵用于隐私保护。
Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance. Due to its computational complexity, most of these machine learning algorithms are offloaded to the cloud for processing. However, users are increasingly concerned about privacy issues such as function creep and malicious usage by third-party cloud providers. To alleviate this, we propose an edge-based filtering stage that removes privacy-sensitive attributes before the sensor data are transmitted to the cloud. We use state-of-the-art image manipulation techniques that leverage disentangled representations to achieve privacy filtering. We define opt-in and opt-out filter operations and evaluate their effectiveness for filtering private attributes from face images. Additionally, we examine the effect of naturally occurring correlations and residual information on filtering. We find the results promising and believe this elicits further research on how image manipulation can be used for privacy preservation.
