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Identifying potential abuses of human rights through imagery is a novel andchallenging task in the field of computer vision, that will enable to exposehuman rights violations over large-scale data that may otherwise be impossible.While standard databases for object and scene categorisation contain hundredsof different classes, the largest available dataset of human rights violationscontains only 4 classes. Here, we introduce the `Human Rights Archive Database'(HRA), a verified-by-experts repository of 3050 human rights violationsphotographs, labelled with human rights semantic categories, comprising a listof the types of human rights abuses encountered at present. With the HRAdataset and a two-phase transfer learning scheme, we fine-tuned thestate-of-the-art deep convolutional neural networks (CNNs) to provide humanrights violations classification CNNs (HRA-CNNs). We also present extensiveexperiments refined to evaluate how well object-centric and scene-centric CNNfeatures can be combined for the task of recognising human rights abuses. Withthis, we show that HRA database poses a challenge at a higher level for thewell studied representation learning methods, and provide a benchmark in thetask of human rights violations recognition in visual context. We expect thisdataset can help to open up new horizons on creating systems able ofrecognising rich information about human rights violations. Our dataset, codesand trained models are available online athttps://github.com/GKalliatakis/Human-Rights-Archive-CNNs.

Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images