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黑盒子Bench:黑盒子对抗性攻击的全面基准
概括
本研究介绍了BlackboxBench,这是一个全面的基准,用于评估针对深度神经网络 (DNN) 的黑子对抗性攻击. 它提供了一个统一的代码库和广泛的评估来分析攻击算法性能,并指导未来的研究.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
- 深度神经网络 深度神经网络
背景情况:
- 深度神经网络 (DNN) 容易受到对抗性示例的影响,因此需要强大的评估方法.
- 黑子对抗性攻击,其中模型细节是未知的,对于现实世界的安全评估至关重要.
- 现有的研究缺乏对黑子攻击算法的全面比较,阻碍了进度分析.
研究的目的:
- 建立一个全面的基准,BlackboxBench,用于评估黑子对抗性攻击.
- 为各种攻击算法提供统一,可扩展和模块化代码库.
- 对当前黑子攻击策略进行彻底的评估和分析.
主要方法:
- 开发了一个统一的代码库,实现了29个基于查询和30个基于转移的对抗性攻击算法.
- 在使用CIFAR-10和ImageNet数据集的主流DNN架构上对这些算法进行了广泛的评估.
- 对算法性能,成功率和查询效率进行了全面的分析.
主要成果:
- 黑盒子Bench方便对59种不同的对抗性攻击算法进行评估.
- 在不同的模型和数据集中进行了超过14,950次评估,产生了广泛的性能数据.
- 该基准提供了对各种攻击方法的优点和缺点的新见解.
结论:
- 黑盒子Bench是理解和推进黑盒子对抗性攻击研究的关键资源.
- 该基准能够进行系统的比较和分析,指导开发更强大的DNN安全性.
- 这项工作为对抗性机器学习的未来路线图提供了关键的分析工具和见解.
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