论文标题
评估四个黑盒对抗攻击和一些查询有效的改进分析
Evaluation of Four Black-box Adversarial Attacks and Some Query-efficient Improvement Analysis
论文作者
论文摘要
随着机器学习技术的快速发展，深度学习模型几乎已经在日常生活的各个方面部署。但是，这些模型的隐私和安全性受到对抗攻击的威胁。在其中，黑框攻击更接近现实，从模型中获得有限的知识。在本文中，我们提供了有关对抗性攻击的基本背景知识，并分析了四种黑盒攻击算法：匪徒，NES，方形攻击和Zosignsgd。我们还探索了新提出的方形攻击方法相对于平方尺寸，希望提高其查询效率。
With the fast development of machine learning technologies, deep learning models have been deployed in almost every aspect of everyday life. However, the privacy and security of these models are threatened by adversarial attacks. Among which black-box attack is closer to reality, where limited knowledge can be acquired from the model. In this paper, we provided basic background knowledge about adversarial attack and analyzed four black-box attack algorithms: Bandits, NES, Square Attack and ZOsignSGD comprehensively. We also explored the newly proposed Square Attack method with respect to square size, hoping to improve its query efficiency.
