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.

翻译：近年来，对抗攻击与防御在计算机视觉系统中引起了越来越多的关注，但迄今为止，大多数研究仍局限于图像领域。然而，许多人工智能模型实际上处理的是文档数据，这与真实世界图像存在显著差异。因此，本工作尝试将对抗攻击理念应用于文档数据和自然数据，并保护模型免受此类攻击。我们重点研究了基于梯度的无目标攻击、基于迁移的攻击和基于得分的攻击，并评估了对抗训练、JPEG输入压缩以及灰度输入变换对ResNet50和EfficientNetB0模型架构鲁棒性的影响。据我们所知，目前学界尚未开展此类工作以研究这些攻击对文档图像分类任务的影响。