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使用深度图像删除隐形图像水印的基线方法

Hengyue Liang1, Taihui Li2, Ju Sun2

  • 1Electrical and Computer Engineering, University of Minnesota, Twin Cities.

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此摘要是机器生成的。

本研究介绍了一种简单的方法,使用Deep Image Prior (DIP) 来从单个图像中删除隐形图像水印. 这种技术有助于检测人工智能生成的内容,并对水标的稳定性进行基准测试.

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科学领域:

  • 计算机视觉 计算机视觉
  • 数字图像处理 数字图像处理
  • 人工智能安全 的人工智能安全

背景情况:

  • 人工智能产生的内容检测对于版权保护和防止错误信息至关重要.
  • 隐形图像水印是一种有希望但易受攻击的内容认证技术.
  • 现有的水印去除方法通常需要大量的数据集或对水印系统的了解.

研究的目的:

  • 开发一种黑子方法来删除隐形图像的水印.
  • 为了评估深度图像先验 (DIP) 对于避开水印的有效性.
  • 提出DIP作为对水标稳定性进行基准测试的基准,并讨论对可见水标的影响.

主要方法:

  • 使用深度图像先验 (DIP) 来删除水印的黑子方法.
  • 通过DIP的中间步骤回归一个单一的水印图像.
  • 与基于培训的可见水印进行基准测试,以评估规避限制.

主要成果:

  • 通过使用DIP.成功地从单个图像中删除了不可见的水印.
  • 在逃避水印过程中实现了高图像质量保护.
  • 证明了黑子方法与基于培训的可见水印的局限性.

结论:

  • DIP提供了一种实用且有效的方法来规避看不见的图像水印.
  • 应将DIP作为评估水印强度的基准.
  • 可见的水印显示出由于当前规避方法的局限性,对抗错误信息的潜力.