mlsanityData to 7 Oct 2026

Evaluating Adversarial Robustness on Document Image Classification

arXiv 2304.12486 ↗First posted 24 Apr 2023cs.CV cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

25 of 33 references.

References · 25

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13 more
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024RoDLA: Benchmarking the Robustness of Document Layout Analysis Modelscited by 12
  2. 2025CHAOS: Chart Analysis with Outlier Samplescited by 1