期刊文献+

基于密集残差网络的图像隐藏方案

Image Hiding Scheme Based on Dense Residual Network
下载PDF
导出
摘要 针对基于编-解码器网络的图像隐写方案生成的含密图像和消息图像质量不高的问题,提出了一种新的基于密集残差连接的编码器-解码器隐写方案,与现有的端到端图像隐写网络不同,所提方案无须对图像进行预处理,采用密集残差连接,将浅层网络的特征输送到深层网络结构的每一层,有效地保留了特征图的细节信息,并使用通道和空间注意力模块对特征进行筛选,提高了编-解码器对图像复杂纹理区域的关注度。在LFW、PASCAL-VOC12和ImageNet数据集的实验结果表明,在保证算法安全性的前提下,所提方法能够有效提高图像质量,含密图像和载体图像的峰值信噪比(peak signal-to-noise ratio,PSNR)和结构相似性(structural similarity,SSIM)的平均值最高达到了36.2 dB和0.98。 A new encod-decoder steganography scheme based on dense residual connection was proposed to solve the problem of poor quality of encod-decoder images and message images generated by image steganography schemes based on encoder-decoder networks.Different from the existing end-to-end image steganography networks,the proposed scheme does not need to preprocess the image,and adopts dense residual connections to transport the features of the shallow network to each layer of the deep network structure,effectively preserving the details of the feature map,and uses channels and spatial attention modules to filter the features,improving the codec s attention to the complex texture region of the image.Experimental results on LFW,PASCAL-VOC12 and ImageNet datasets show that the proposed method can effectively improve image quality under the premise of ensuring the security of the algorithm,including the peak signal-to-noise ratio of dense images and carrier images.The mean values of peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)are 36.2 dB and 0.98 respectively.
作者 陈立峰 刘佳 潘晓中 孙文权 董炜娜 CHEN Li-feng;LIU Jia;PAN Xiao-zhong;SUN Wen-quan;DONG Wei-na(College of Cryptography Engineering,Engineering University of PAP,Xi'an 710086,China)
出处 《科学技术与工程》 北大核心 2024年第9期3719-3726,共8页 Science Technology and Engineering
基金 国家自然科学基金面上项目(62272478) 国家自然科学基金(61872384,62102451)。
关键词 信息隐藏 深度学习 注意力机制 编码-解码结构 密集残差网络 information hiding deep learning attention mechanism encoding-decoding structure dense residual network
  • 相关文献

参考文献8

二级参考文献29

共引文献65

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部