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Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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敌对攻击数据集用于低光图像增强.

Axel Martinez1, Matthieu Olague2, Gustavo Olague1

  • 1Department of Computer Science, CICESE, Ensenada, 22860, México.

Data in brief
|June 12, 2025
PubMed
概括

这项研究引入了一个新的数据集,用于评估低光图像增强算法,以防止对抗性攻击. 该数据集有助于提高深度学习模型在自动驾驶和监控等关键应用中的可靠性.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 图像处理 图像处理

背景情况:

  • 低光图像增强对于监控和自动驾驶等应用至关重要.
  • 深层卷积神经网络 (CNN) 是有效的,但易受对抗性攻击的影响,这给可靠性带来了挑战.

研究的目的:

  • 创建一个全面的数据集来评估低光图像增强算法的对抗性稳定性.
  • 为研究人员提供一个资源,以测试和提高图像增强系统的安全性.

主要方法:

  • 通过对五个已建立的图像数据库 (MEF,LIME,LOLv1,LOLv2-R,LOLv2-S) 应用12种类型的对抗性攻击,开发了4970张数字图像的数据集.
  • 包含原始图像与对抗性示例一起用于直接比较和分析.
  • 该数据集涵盖了各种场景,包括不同照明条件下的景观,物体和位置.

主要成果:

  • 该存储库提供了一个标准化的基准来评估低光图像增强中的对抗性稳定性.
  • 有助于研究漏洞和开发更有弹性的深度学习模型.

结论:

  • 拟议的数据集对于推进强大的低光图像增强研究至关重要.
关键词:
对抗性的例子.敌对的强度 敌对的强度深度学习是一种深度学习.象征性学习是一种象征性学习.

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  • 解决对抗性漏洞对于在现实应用中可靠地部署这些技术至关重要.