Author

M.A. Baronov

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Preprint Aug 2026

GENADA: efficient generative time series adversarial attack framework

A GENerative ADversarial Attack (GENADA) is proposed that learns a generative model to produce deceptive perturbations directly in a single forward pass and a procedure to train it, and achieves comparable attack quality to strong baselines while requiring less time to generate perturbations during inference.

M.A. Baronov, D. Vorobev, M. Rusanova et al. · 0 citations