一种基于神经网络的水印方法,接近JPEG量子化
Shingo Yamauchi1, Masaki Kawamura1
1Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 753-8512, Japan.
Journal of imaging
|June 26, 2024
概括
这项研究引入了一种新的神经网络水印方法,使用量子化激活函数来准确模拟JPEG压缩. 这种方法提高了对JPEG压缩攻击的稳定性,在图像质量和比特错误率方面表现优于传统方法.
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
- 机器学习 机器学习
背景情况:
- 基于神经网络的水印对于数字内容的保护至关重要.
- 传统的方法往往模拟JPEG压缩不准确地使用噪声添加.
- 现有的方法缺乏对实际的JPEG压缩工件的强大性能.
研究的目的:
- 开发一种神经网络水印方法,提高了对JPEG压缩的稳定性.
- 引入一种新的量子化激活函数,可以准确地模仿JPEG量子化.
- 通过更有效的水印技术来提高数字图像安全性.
主要方法:
- 提出了一种新型的量子化激活函数,该函数由形 hyperbolic 函数组成.
- 将量子化激活功能集成到ReDMark网络的攻击层中.
- 与使用标准和定量化激活功能处理图像的传统ReDMark相比,性能进行了比较.
主要成果:
- 拟议的量子化激活函数准确地接近JPEG压缩.
- 使用量子化激活功能的网络表现出对JPEG压缩的优越稳定性.
- 与ReDMark.com相比,新方法实现了较低的比特错误率 (BER).
结论:
- 新的量子化激活功能显著提高了神经网络水印的稳定性,对抗JPEG压缩.
- 这种方法提供了比现有技术更好的图像质量和更低的BER.
- 这种方法代表了安全的数字图像水印的重大进步.
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