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Privacy Attributes-aware Message Passing Neural Network for Visual Privacy Attributes Classification

机译:隐私属性 - 感知消息通过神经网络进行Visual Privacy属性分类

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摘要

Visual Privacy Attribute Classification (VPAC) identifies privacy information leakage via social media images. These images containing privacy attributes such as skin color, face or gender are classified into multiple privacy attribute categories in VPAC. With limited works in this task, current methods often extract features from images and simply classify the extracted feature into multiple privacy attribute classes. The dependencies between privacy attributes, e.g., skin color and face typically coexist in the same image, are usually ignored in classification, which causes performance degradation in VPAC. In this paper, we propose a novel end-to-end Privacy Attributes-aware Message Passing Neural Network (PA-MPNN) to address VPAC. Privacy attributes are considered as nodes on a graph and an MPNN is introduced to model the privacy attribute dependencies. To generate representative features for privacy attribute nodes, a class-wise encoder-decoder is proposed to learn a latent space for each attribute. An attention mechanism with multiple correlation matrices is also introduced in MPNN to learn the privacy attributes graph automatically. Experimental results on the Privacy Attribute Dataset demonstrate that our framework achieves better performance than state-of-the-art methods for visual privacy attributes classification.
机译:Visual Privacy属性分类(VPAC)通过社交媒体图像识别隐私信息泄露。这些包含隐私属性(如皮肤颜色,脸部或性别)的图像被分类为VPAC中的多个隐私属性类别。使用有限的作品在此任务中,当前方法通常会从图像中提取功能,并将提取的功能分类为多个隐私属性类。隐私属性之间的依赖性,例如,肤色和面部通常在同一图像中共存,通常在分类中忽略,这导致VPAC中的性能下降。在本文中,我们提出了一种新颖的端到端隐私属性感知消息,通过神经网络(PA-MPNN)来解决VPAC。隐私属性被视为图形上的节点,并引入MPNN以模拟隐私属性依赖项。为了生成隐私属性节点的代表特征,提出了一种类WISE编码器解码器来学习每个属性的潜空间。在MPNN中也引入了具有多个相关矩阵的注意机制,以自动学习隐私属性图。隐私属性数据集上的实验结果表明,我们的框架比视觉隐私属性分类的最先进方法实现了更好的性能。

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