论文标题
基于内容的图形隐私顾问
Content-based Graph Privacy Advisor
论文作者
论文摘要
人们可能不知道在线上载图像的隐私风险。在本文中，我们介绍了图形隐私顾问，这是图像隐私分类器，使用场景信息和对象基数作为预测图像是否私有的线索。 Graph隐私顾问简化了最先进的图形模型，并通过完善从图像中提取的信息的相关性来提高其性能。我们通过用较低维度，更有效的功能替换高维图像向量来确定用于隐私分类任务的最有用的视觉功能，并降低模型的复杂性。我们还通过对对象共发生建模而不是每个类中对象出现的频率来解决偏见的先前信息问题。
People may be unaware of the privacy risks of uploading an image online. In this paper, we present Graph Privacy Advisor, an image privacy classifier that uses scene information and object cardinality as cues to predict whether an image is private. Graph Privacy Advisor simplifies a state-of-the-art graph model and improves its performance by refining the relevance of the information extracted from the image. We determine the most informative visual features to be used for the privacy classification task and reduce the complexity of the model by replacing high-dimensional image feature vectors with lower-dimensional, more effective features. We also address the problem of biased prior information by modelling object co-occurrences instead of the frequency of object occurrences in each class.
