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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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一种看不见的,强大的版权保护方法,用于DNN生成的内容.

Donghua Wang1, Wen Yao2, Tingsong Jiang2

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou, China; Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|May 22, 2024
PubMed
概括

本研究介绍了一种使用深度神经网络 (DNN) 进行数字图像版权保护的新隐形方法. 该技术强大嵌入版权信息,确保图像质量和在社交媒体上的实际可行性.

关键词:
版权保护 版权保护一个看不见的扰动.坚固性技术 坚固性技术

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数字图像处理 数字图像处理

背景情况:

  • 深度神经网络 (DNN) 广泛用于图像应用,需要强大的版权保护.
  • 传统的可见水印方法通常会降低图像质量,并引入文物.
  • 现有的隐形水印技术可能缺乏对常见图像扭曲的稳定性.

研究的目的:

  • 为DNN产生的图像提出一种新的隐形和强大的版权保护方法.
  • 在嵌入版权信息后,确保图像的美学质量.
  • 在现实场景中验证方法的有效性和实际可行性.

主要方法:

  • 两个网络架构:一个版权编码器和一个版权解码器.
  • 编码器将版权信息作为图像中的隐形扰动嵌入到图像中.
  • 一个强度模块增强了解码器从扭曲图像中提取信息的能力.

主要成果:

  • 提出的方法有效地嵌入和提取版权信息.
  • 编码的图像保持高的美学质量.
  • 强度模块显著提高了对各种图像扭曲的弹性.

结论:

  • 新的隐形版权保护方法是有效和强大的.
  • 该技术保留了图像质量,并通过社交媒体测试证明了实际可行性.
  • 这种方法在人工智能生成图像时代为版权保护提供了有希望的解决方案.