基于完全反传播神经网络(FCNN)的数字水印新算法

数字水印对于保护和鉴定多媒体信息是一项重要技术,它可以在非法复制和篡改中保护数字媒体.数字水印的嵌入与检测涉及到复杂的程序,其中包括对水印的随机化,选择水印的嵌入位置和检测位置,将随机水印嵌入到特殊位置并在此位置上检测水印等.本文设计了一个完全反传播神经网络方法,水印通过特殊的完全反传播神经网络嵌入于检测水印.区别于传统的方法,水印嵌入到FCNN的神经元上代替了嵌入在宿主图像上,因此数字水印图像几乎与宿主图像一样,大部分攻击不可能降低嵌入水印质量.实验结果表明,此方案能够达到鲁棒性、不可见性与真实性.

New algorithm for digital watermark based on a full counter-propagation neural network

Digital watermarks are an important technique for protection and identification.Watermark has been developed to protect digital media from being illegally reproduced and modified.Embedding and extracting watermark requires complex procedures.These include randomizing the watermark,choosing positions to embed and extract it,embedding the randomized watermark into the specific positions,and extracting it from the specific positions.In this paper,a novel method called full counter-propagation neural network(FCNN) for digital image watermark is proposed,in which the watermark is embedded and extracted through specific FCNN.Different from the traditional methods,the watermark is embedded in the synapses of FCNN instead of the cover image.Therefore,the watermark image is almost the same as the cover image.In addition,most of the attacks could not degrade the quality of the extracted watermark image.The experiment results show that the proposed method is able to achieve robustness,imperceptibility and authenticity in watermark.