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
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.
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.
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