Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing

AITE (Adversarial Iterative Temporal Editing) attacks online handwriting recognizers by inserting and deleting points along the pen trajectory — guided by Grad-CAM salience — rather than adding pixel-wise noise. This preserves stroke shape while achieving strong black-box transferability across CNN, BLSTM, and Transformer targets.

Installation

Requirements: Python ≥ 3.9, PyTorch ≥ 2.0, CUDA 11.8+ (≥ 6 GB VRAM recommended).

git clone https://github.com/yataro0117/AITE.git
cd AITE
pip install -e .

To pin exact versions for full reproducibility:

pip install torch==2.3.0 torchvision==0.18.0 --index-url https://download.pytorch.org/whl/cu118
pip install -e ".[dev]"

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Citation

@inproceedings{tamura2026aite,
  title     = {Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing},
  author    = {Tamura, Yataro and Iwana, Brian Kenji and Lee, Jiseok},
  booktitle = {Proceedings of the International Conference on Document Analysis and Recognition (ICDAR)},
  year      = {2026},
  eprint    = {2607.12500},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}

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License

This project is released under the Apache-2.0 License. See NOTICE for third-party attributions (CleverHans, Pialla et al. smoothness metric).