  Adversarial attacks and defenses have gained increasing interest on computer
vision systems in recent years, but as of today, most investigations are
limited to images. However, many artificial intelligence models actually handle
documentary data, which is very different from real world images. Hence, in
this work, we try to apply the adversarial attack philosophy on documentary and
natural data and to protect models against such attacks. We focus our work on
untargeted gradient-based, transfer-based and score-based attacks and evaluate
the impact of adversarial training, JPEG input compression and grey-scale input
transformation on the robustness of ResNet50 and EfficientNetB0 model
architectures. To the best of our knowledge, no such work has been conducted by
the community in order to study the impact of these attacks on the document
image classification task.
