Graph-based neural networks for explainable image privacy inference

Highlights：

• We propose an improved dataset for research on image privacy.

• We build a knowledge graph that represents the relevance between the objects and image privacy.

• Based on the graph, we deal with the traditional image classification problem with the novel graph-based neural network.

• The introduction of the knowledge graph not only makes the model more explainable but also makes better use of the information of objects provided by the images.

• Our method relies on the intrinsic relevance in the dataset rather than extra knowledge, thus is applicable for other tasks.