数字水印的攻击方法

数字水印是近两年来出现的数字产品版权保护技术,目的是保护数字产品的合法拷贝和传播,是当前国际学术界的研究热点。数字水印包括算法设计和攻击技术两方面,它们是互相促进的对立面。本文简单讨论了数字水印的概念和应用,全面分析总结了当前数字水印的各种攻击方法,提出了数字水印下一步的发展方向,对改进和设计水印算法具有重要的指导作用。     

一种基于分块DCT的盲灰度水印算法

提出了一种在分块DCT的次低频交流系数中,利用奇偶特性和JPEG量化系数嵌入有含义的8位灰度水印图像,以实现盲提取的算法,并通过PN序列加密来保证水印的安全性。实验结果表明,该算法对于JPEG有损压缩、图像剪切等处理有很强的鲁棒性。     

数字水印的解释攻击与关联性特征

数字水印作为一种版权保护技术已经成为目前的研究热点,引起了越来越多的关注;同时,对于数字水印的攻击也在不断出现,其中以解释攻击（IBM攻击）最为危险。通过对解释攻击进行分析,首次提出了水印的关联性特征对水印嵌入进行规范,并提出了两种构造水印关联性特征的方式：利用水印信息内容和利用水印嵌入定位。通过对利用信息内容的方式进行测试,如果所有水印嵌入都能够遵守水印关联性特征,那么就可以解决解释攻击问题。     

多方共享版权的数字水印方案

基于数字水印和密码技术,提出一种由多方共同享有版权的数字媒体版权保护技术。根据可信水印中心存在与否,提出两个多方共享版权的水印方案,经分析,这两个方案均是可行和安全的。     

Wavelet-FCWAN: Fast and Covert Watermarking Attack Network in Wavelet Domain

The protection of digital image content has become an important topic of scientific research with the continuous development and updates of Internet and multimedia technology. In the past few years, various watermarking algorithms with good robustness and imperceptibility have been proposed, but the development of watermarking attack techniques has stagnated. In this paper, we have attempted to use deep learning to develop a new watermarking attack scheme and present the Fast and Covert Watermarking Attack Network in Wavelet Domain (Wavelet-FCWAN). The watermarking attack scheme employs noise filling as a preprocessing step for the watermarked image, performs wavelet transform operation on the preprocessed watermarked image, and inputs the wavelet transformed sub-image into Wavelet-FCWAN along with the noise level map in parallel. The network can be trained quickly and produces a better watermarking attack effect while ensuring the visual quality and detail retention of the attacked image. The experiments show that Wavelet-FCWAN demonstrates the superiority of its watermarking attack effect by comparing different attack strategies, and it can produce varying degrees of attack effects on various image watermarking algorithms with high levels of universality and imperceptibility.