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相关概念视频

Understanding Deception01:14

Understanding Deception

146
Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
146
Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Impact of a serious immersive virtual reality game in managing pain during venous or catheter procedures in Pediatric Oncology.

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相关实验视频

Updated: Jan 10, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

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通过多视角可解释的人工智能检测雕伪造.

Miguel José das Neves1, Felipe Rodrigues Perche Mahlow1, Renato Dias de Souza1

  • 1School of Sciences, São Paulo State University (UNESP), Bauru 17033-360, Brazil.

Journal of imaging
|November 26, 2025
PubMed
概括

这项研究引入了E-XAI,这是一个可解释的框架,用于检测像雕刻伪造等图像操纵. 它通过解释卷积神经网络 (CNN) 决策,增强对数字取证人工智能的信任.

关键词:
这就是 SHAP SHAP 的意思.卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.可解释的人工智能 (XAI)图像取证医学 图像取证医学接雕刻 接雕刻 接雕刻

相关实验视频

Last Updated: Jan 10, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.7K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数字法医学数字法医学

背景情况:

  • 像CNN这样的深度学习模型在检测图像操纵方面表现有前途,但缺乏透明度.
  • 美国有线电视新闻的黑盒子性质阻碍了对关键应用程序 (如数字取证) 的信任.
  • 内容意识的图像伪造,如雕刻,对检测构成重大挑战.

研究的目的:

  • 开发和验证一个可解释的框架来检测图像伪造,特别是雕刻.
  • 通过提供可解释的决策,提高数字取证中的AI系统的可信度.
  • 解决当前深度学习模型在为伪造检测提供透明解释方面的局限性.

主要方法:

  • 提出了一个新的框架,E-XAI (Ensemble Explainable AI),集成多种可解释性技术.
  • 结合SHAP (SHapley增量扩展) 进行像素级特征赋值和Grad-CAM进行区域级本地化.
  • 训练了一台定制的CNN,并在10300张图像的平衡数据集上验证了E-XAI框架.

主要成果:

  • 在一个看不见的测试机组上实现了高分类性能,准确率为95%和精度为99%的造类.
  • 证明了框架对JPEG压缩的稳定性,这是一个常见的现实世界扰动.
  • E-XAI提供了透明的视觉证据,证明模型如何识别微妙的伪造文物,提高了可解释性.

结论:

  • E-XAI框架提供了一个强大的端到端管道,用于可解释的图像伪造检测.
  • 整合可解释性技术可以提高AI在信息安全方面的可靠性和可信度.
  • 这种方法为CNN对图像操纵检测的决策过程提供了关键的见解.