深度学习下动态图像无损压缩加密算法设计  

Design of Lossless Compression Encryption Algorithm for Dynamic Image under Deep Learning

作　　者：李向荣 化秀玲[2] 李振东 LI Xiang-rong;HUA Xiu-ling;LI Zhen-dong(School of Computer and Information Engineering,Xinxiang University,Xinxiang Henan 453003,China;North China University of Water Resources and Electric Power,Zhengzhou Henan 450045,China)

机构地区：[1]新乡学院计算机与信息工程学院,河南新乡453003 [2]华北水利水电大学,河南郑州450045

出　　处：《计算机仿真》2024年第9期146-150,479,共6页Computer Simulation

摘　　要：图像压缩的目的是减少图像数据中的冗余信息,从而使其更加高效、安全的存储和传输。由于动态图像中存在敏感信息,在传输过程中容易导致数据受损或丢失,为了增加动态图像无损压缩加密的安全性,提出一种基于深度学习的动态图像无损压缩加密方法。分析动态图像冗余信息的分布规律,建立预测模板,对变换后的数据采用差分脉冲编码调制(Differential Pulse Code Modulation,DPCM)展开无损编码处理。通过深度学习算法对动态图像特征提取,利用动态图像的个体特征形成密钥,将其应用到动态图像加密中,进而完成动态图像无损压缩加密。实验结果表明,所提方法的峰值信噪比高、压缩比在1.7%以上,表明所提方法对动态图像无损压缩具有优越性,且加密效果好、安全性高。Image compression is aimed at reducing redundant information in image data,making it more efficient and secure for storage and transmission.Since sensitive information exists in dynamic images,it is easy to cause damage or loss of data during transmission.In order to enhance the security of lossless compression encryption of dynamic images,a method for lossless compression encryption of dynamic images was proposed based on deep learning.Firstly,the distribution rules of redundant information in dynamic images were analyzed,and then the prediction templates were constructed.Differential Pulse Code Modulation(DPCM) was used for lossless encryption of transformed data.Moreover,the deep learning algorithm was used to extract features from dynamic images.Furthermore,individual features of dynamic images were used to form a key.After that,it was applied to dynamic image encryption.Finally,the lossless compression encryption of dynamic images was achieved.Experimental results show that the proposed method has a high peak signal-to-noise ratio,and the compression ratio is more than 1.7%,indicating its superiority in dynamic image lossless compression,high encryption effect as well as security.

关 键 词：深度学习 动态图像 无损压缩 加密 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]