论文标题
基于增强学习的互动推荐系统的对抗性攻击和检测
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
论文作者
论文摘要
对抗性攻击在早期发现对抗性攻击方面面临着重大挑战。我们提出了基于增强学习的交互式推荐系统的攻击敏捷检测。我们首先制作对抗性示例，以通过基于精心设计的数据来检测基于深度学习的分类器的潜在攻击来显示它们的各种分布，然后增强建议系统。最后，我们研究了对抗性示例的攻击强度和频率，并通过多种制定方法在标准数据集上评估了我们的模型。我们广泛的实验表明，大多数对抗性攻击都是有效的，并且攻击强度和攻击频率都会影响攻击性能。策略性地定时攻击仅以1/3至1/2的攻击频率实现了比较攻击性能。此外，我们经过一种手工制作方法训练的黑盒检测器具有多种制作方法的概括能力。
Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several crafting methods.
