Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing
Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications. However, their vulnerability to adversarial attacks is still a challenge. Existing adversarial methods are predominantly designed for image-based inputs and typically rely on additive spatial perturbations. When applied to online handwriting, which is inherently represented as a time series of pen trajectories, such perturbations often introduce high-frequency jitter and visibly unnatural stroke artifacts. In this work, we propose a novel adversarial attack framework for online handwriting recognition based on salience-guided temporal editing. Instead ...
| Authors | Yataro Tamura, Brian Kenji Iwana, Jiseok Lee |
|---|---|
| Primary category | cs.LG |
| Published | 14 July 2026 |
| Last updated at source | 19 September 2026 |
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