Semi-automated Disaster Image Tagging While Protecting Privacy: A Case Study

Disaster digital archives play an important role in preserving and disseminating a wide range of natural disasters data. Image tagging is required to effectively manage the archives, and nowadays machine learning can help us tag images efficiently. When training machine learning models, image classification models pre-trained with data of other disasters may enable us to have an effective model quickly. However, there is a risk of information leakage in this pre-trained model exploitation, such as image inversion. We should note that such risks must be serious in disaster digital archives. This paper focuses on protecting the privacy of trained models, particularly protecting data from hostile attacks. A key idea of this paper is to train an image classification model using images that are visually privacy protected while remaining important features for training, thereby avoiding the risk of information leakage in sharing the model. We examine our proposal in two use-cases: one in which a user wants to train a model to share, and the other in which a user receives a pre-trained model to be used as a base for constructing an archive. This study is applied to a real-world test case with sensitive data from two disasters: the 2011 Great East Japan Earthquake and the 2016 Kumamoto Earthquake.