论文标题
对1D模型的对抗性攻击的深度对抗性防御
A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models
论文作者
论文摘要
由于攻击者的脆弱性，深度学习算法最近已成为攻击者的针对性。已经进行了一些研究来解决这个问题并建立更强大的深度学习模型。非连续的深层模型仍然不适合对抗，在这些研究中，大多数最近的研究都集中在开发攻击技术以逃避模型的学习过程。此类模型脆弱性背后的主要原因之一是学习分类器无法稍微预测扰动的样本。为了解决这个问题，我们提出了一种新的客观/损失函数，即所谓的边缘对比，该功能强制执行在指定边缘下的特征，以使用深层卷积网络（即char-cnn）来促进其预测。已经对连续案例（例如UNSW NB15数据集）和离散案例（即八大规模数据集[32]）进行了广泛的实验，以证明该方法的有效性。结果表明，基于提出的损失函数的学习过程的正则化可以改善CHAR-CNN的性能。
Deep learning algorithms have been recently targeted by attackers due to their vulnerability. Several research studies have been conducted to address this issue and build more robust deep learning models. Non-continuous deep models are still not robust against adversarial, where most of the recent studies have focused on developing attack techniques to evade the learning process of the models. One of the main reasons behind the vulnerability of such models is that a learning classifier is unable to slightly predict perturbed samples. To address this issue, we propose a novel objective/loss function, the so-called marginal contrastive, which enforces the features to lie under a specified margin to facilitate their prediction using deep convolutional networks (i.e., Char-CNN). Extensive experiments have been conducted on continuous cases (e.g., UNSW NB15 dataset) and discrete ones (i.e, eight-large-scale datasets [32]) to prove the effectiveness of the proposed method. The results revealed that the regularization of the learning process based on the proposed loss function can improve the performance of Char-CNN.
