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偷偷和高效的对抗性例子攻击视频检索系统.

Xin Yao1, Enlang Li1, Yimin Chen2

  • 1School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.

Neural networks : the official journal of the International Neural Network Society
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PubMed
概括
此摘要是机器生成的。

我们介绍了DUO,这是一个针对视频检索系统的新型对抗性示例 (AE) 攻击. 对于隐蔽和高效的攻击,DUO产生了稀疏的干扰,显著优于现有的方法.

关键词:
黑子攻击黑子攻击偷偷的 偷偷的 偷偷的 偷偷的有针对性的稀少的对抗性攻击例子攻击目标.视频检索系统的视频检索系统.

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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:

  • 专注于视频的社交媒体推动了对高效视频检索系统的需求.
  • 机器学习为这些系统提供动力,但易受对抗式示例 (AE) 攻击.
  • 由于广泛的干扰,现有的视频AE攻击通常可以被检测出来.

研究的目的:

  • 提出DUO,一个隐蔽的,有针对性的,黑子AE攻击视频检索系统.
  • 为了提高AE攻击中的隐形性和查询效率.
  • 开发稀疏扰动生成方法,以提高AE攻击性能.

主要方法:

  • 对于稀疏的干扰,DUO使用了一个DUal搜索Over像素策略.
  • 关键观察包括关键视频的重要性,像素/贡献差异以及像素位置.
  • 建议使用SparseTransfer和SparseQuery组件的两个变体DUO_P (像素稀疏性) 和DUO_G (组稀疏性).

主要成果:

  • 与现有的AE攻击相比,DUO显示出更好的隐形性和有效性.
  • DUO_P实现了更高的精度,并减少了超过100倍的对抗性干扰.
  • DUO_G需要比最先进的方法少10倍多的查询.

结论:

  • 在隐蔽和高效的对抗性攻击中,DUO对视频检索系统提供了显著的进步.
  • 提出的基于稀疏性的方法有效地减少了干扰和查询数量.
  • DUO为评估和提高视频检索技术安全性提供了一个强大的框架.