  Time series classification (TSC) has emerged as a critical task in various
domains, and deep neural models have shown superior performance in TSC tasks.
However, these models are vulnerable to adversarial attacks, where subtle
perturbations can significantly impact the prediction results. Existing
adversarial methods often suffer from over-parameterization or random logit
perturbation, hindering their effectiveness. Additionally, increasing the
attack success rate (ASR) typically involves generating more noise, making the
attack more easily detectable. To address these limitations, we propose SWAP, a
novel attacking method for TSC models. SWAP focuses on enhancing the confidence
of the second-ranked logits while minimizing the manipulation of other logits.
This is achieved by minimizing the Kullback-Leibler divergence between the
target logit distribution and the predictive logit distribution. Experimental
results demonstrate that SWAP achieves state-of-the-art performance, with an
ASR exceeding 50% and an 18% increase compared to existing methods.
