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Updated: Jul 10, 2025

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Visualizing Visual Adaptation
Published on: April 24, 2017
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低通图像过以实现对抗性稳定性
Vadim Ziyadinov1, Maxim Tereshonok1,2
1Science and Research Department, Moscow Technical University of Communications and Informatics, 111024 Moscow, Russia.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
我们提出一种低通图像过技术,以提高卷积神经网络 (CNN) 对抗对方攻击的稳定性. 这种方法通过减少高频噪声来提高图像识别的准确性,模仿人类的视觉感知.
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:
- 卷积神经网络 (CNN) 容易受到对抗性攻击,这引入了不可察觉的噪音,大大降低了准确性.
- 机器图像感知严重依赖于高频扭曲,与人类感知不同,它专注于整体对象形状.
- 现有的研究重点是提高CNN对抗对抗干扰的稳定性.
研究的目的:
- 开发一种技术,以减少高频噪声对CNN的影响.
- 为了提高图像识别系统的噪声免疫力和稳定性.
- 为了使CNN的感知逻辑与人类视觉处理保持一致,以提高强度.
主要方法:
- 实施低通图像过以减轻高频扭曲.
- 在对抗性攻击下评估过对CNN准确性的影响.
- 将CNN的表现与未使用拟议的过技术进行比较.
主要成果:
- 低通波可以显著提高在存在高频扭曲时的图像识别精度,特别是来自对手攻击的高频扭曲.
- 拟议的技术证明了资源效率和易于实施.
- 过有助于CNN更好地忽略高频噪音,类似于人类的视觉感知.
结论:
- 低通图像过是一种有效的方法,可以增强CNN对敌对攻击的稳定性.
- 这种技术提供了一种实际的方法来提高图像识别系统中的噪声免疫力.
- 这项研究表明,在人工智能中开发更类似人类的图像感知是一种途径.
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