Deep neural network (DNN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks have achieved good performance in feed-forward model and image recognition tasks, the extension for time series classification in the recurrent neural network (RNN) remains a dilemma, because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity to perturbations of time series data challenges the traditional local optimization objective to minimize perturbation. In this paper, an efficient and widely applicable approach called TSFool for crafting high-quality adversarial time series for the RNN classifier is proposed. We propose a novel global optimization objective named Camouflage Coefficient to consider how well the adversarial samples hide in class clusters, and accordingly redefine the high-quality adversarial attack as a multi-objective optimization problem. We also propose a new idea to use intervalized weighted finite automata (IWFA) to capture deeply embedded vulnerable samples having otherness between features and latent manifold to guide the approximation to the optimization solution. Experiments on 22 UCR datasets are conducted to confirm that TSFool is a widely effective, efficient and high-quality approach with 93.22% less local perturbation, 32.33% better global camouflage, and 1.12 times speedup to existing methods.
翻译:深心神经网络分类(DNN)很容易受到对抗性攻击。虽然现有的基于梯度的攻击在饲料向向模式和图像识别任务方面取得了良好的效果,但延长经常性神经网络的时间序列分类仍是一个两难问题,因为RNN的周期性结构防止了直接的模式差异和对时间序列数据扰动的视觉敏感性,挑战了传统的本地优化目标,以尽量减少扰动。在本文件中,提出了一种高效和广泛应用的方法,称为TSFool,为RNN分类者设计高质量的对抗时间序列。我们提出了一个名为Camouflavere Covalt的新的全球优化目标,以考虑对抗性标本在类中隐藏的样本有多好,并因此将高质量的对抗性攻击重新定义为多目标优化问题。我们还提出了一个新想法,即使用间隔加权定限值自动图(IWFA)来捕捉深嵌入的脆弱样本,这些样本具有其他特性和潜伏式的特性,用以指导对准优化解决方案。在22 UCRD数据集上进行的实验旨在证实TSFool是广泛有效、高效和高质量的百分度为93至22。