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