使用CNN和移动窗口功能提取的绘画身份验证
Juan Ruiz de Miras1, José Luis Vílchez2, María José Gacto1,3,4
1Software Engineering Department, University of Granada, Granada, Spain.
Frontiers in artificial intelligence
|January 29, 2026
概括
这项研究使用了定制卷积神经网络 (CNN) 来验证Paolo Veronese绘画的真实性. 基于补丁的方法实现了94.51%的准确性,提供了对风格特征的洞察力.
科学领域:
- 艺术品的认证 艺术品的认证
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习是机器学习.
背景情况:
- 绘画的真实性是复杂的,它结合了知识和技术分析.
- 数据稀缺性给计算身份验证方法带来了挑战.
- 风格分析需要专注于艺术执行,而不是主题.
研究的目的:
- 用新的CNN方法来验证一幅归因于保罗·维罗内斯的画作.
- 通过基于补丁的战略解决艺术品认证中的严重数据短缺问题.
- 分析微纹理和笔触特征以进行风格比较.
主要方法:
- 开发了一个定制的浅卷积神经网络 (CNN),用于多通道图像输入 (RGB,灰度,边缘图).
- 采用滑动窗口策略来提取重叠的补丁,扩展数据集.
- 利用规范化技术进行概括和绘画级交叉验证,以防止数据泄露.
主要成果:
- 实现了高分类性能:94.51%的准确性和0.99曲线下的面积 (AUC).
- 生成了概率热图,突出了真实作品的风格连贯性和其他作品的碎片化.
- 被检查的绘画显示部分风格亲和 (61%的概率) 与真实的维罗纳作品.
结论:
- 基于补丁的CNN模型对于艺术认证中的风格分析是有效的,特别是在数据有限的情况下.
- 该模型提供了风格亲和力的概率证据,补充了传统的身份验证方法.
- 确定归因需要将人工智能驱动的风格分析与历史,技术和来源数据相结合.
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