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专家们无法可靠地检测到人工智能生成的组织学数据.

Jan Hartung1,2,3,4, Stefanie Reuter5, Vera Anna Kulow6

  • 1Institute for Physiology, Faculty of Medicine, University of Freiburg, 79108, Freiburg, Germany. jan.hartung@physiologie.uni-freiburg.de.

Scientific reports
|November 20, 2024
PubMed
概括
此摘要是机器生成的。

人工智能现在可以生成真实的组织学图像,甚至可以愚弄科学专家. 这一突破需要新的方法和政策来检测科学出版物中的AI生成数据.

关键词:
人工智能的人工智能是人工智能.欺诈 欺诈 欺诈 欺诈 欺诈历史学 历史学 历史学不当的行为 不当的行为出版 出版 出版 出版 出版稳定的扩散 稳定的扩散

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科学领域:

  • 数字病理学数字病理学
  • 人工智能的人工智能是人工智能.
  • 图像的取证医学.

背景情况:

  • 人工智能图像生成正在迅速发展,这给图像真实性带来了挑战.
  • 人工智能生成的图像可以模仿复杂的结构,如组织学样本,这引发了对数据制造的担忧.

研究的目的:

  • 评估人类和专家的能力,以区分人工智能生成的组织学图像与真实的图像.
  • 评估稳定扩散在制造令人信服的人工组织学样本方面的有效性.

主要方法:

  • 利用稳定扩散,最近的一种生成算法,创建人工组织学样本.
  • 对800多名参与者进行了一项研究,以测试真实和人工智能生成的图像之间的歧视.
  • 基于培训数据量分析了参与者的表现.

主要成果:

  • 即使是专家也无法可靠地区分真实和人工智能生成的组织学图像.
  • 参与者的表现随着训练而有所改善,但仍然不完美.
  • 即使使用少量的训练数据,也可以生成令人信服的人工图像.

结论:

  • 目前的AI图像生成技术可以产生高度欺骗性的组织学图像.
  • 现有的人类感知能力不足以可靠地检测人工智能制造的科学数据.
  • 迫切需要开发检测方法和改变政策,以确保科学出版物中的数据完整性.