在深度排名中的对抗性攻击和防御
IEEE transactions on pattern analysis and machine intelligence
|February 13, 2024
概括
图像排名中的深度神经网络 (DNN) 容易受到对抗性攻击. 这项研究引入了新的攻击和防御机制,显著提高了排名系统对干扰的稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 容易受到对抗性攻击,导致错误分类.
- 基于DNN的图像排名系统对此类攻击的脆弱性尚未得到充分了解.
研究的目的:
- 提出针对深层排名系统的新型对抗性攻击 (候选人攻击,查询攻击).
- 开发一个有效的防御机制 (抗崩三重防御),以提高模型的稳定性.
主要方法:
- 将排名顺序表示为不等式,并设计一个三重函数的客观函数,以获得最佳的扰动.
- 开发一种抗崩三重防御,以防止正负样本崩.
- 提出一个实证的强度评分来评估对各种攻击的防御有效性.
主要成果:
- 证明拟议的攻击可以有效地破坏深层排名系统.
- 展示了拟议的防御方法显著提高了排名系统的稳定性.
- 在多个数据集中验证了有效性 (MNIST,时尚-MNIST,CUB200-2011,CARS196,斯坦福在线产品).
结论:
- 深度排名系统容易受到特定的对抗性攻击.
- 拟议的防御战略增强了强度,并减轻了广泛的攻击.
- 这项工作有助于保护基于DNN的图像排名应用程序.
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