Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing

arXiv:2607.12500 · cs.LG, cs.CV · Submitted 2026-07-14 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-07-14

Updated: 2026-09-19

Comments: Accepted at ICDAR 2026

Code: https://github.com/yataro0117/AITE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adversarial Attacks — 44× in this paper · explained in 1 episode
• 1D CNN — 21× in this paper · explained in 2 episodes
• Transferability — 16× in this paper · explained in 4 episodes
• Adversarial Examples — 13× in this paper · explained in 2 episodes
• Transformer — 13× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Robustness — 4× in this paper · explained in 5 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Adversarial Noise — 3× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adversarial Iterative Temporal Editing — 2× in this paper
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Basic Iterative Method — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Projected Gradient Descent — 1× in this paper
• Structural Similarity Index Measure — 1× in this paper
• Temporal Convolutional Neural Networks — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
• Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
• Intriguing properties of neural networks

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