基于深度学习的混沌加密灰度图像重建方法  

Reconstruction of Chaotic Grayscale Image Encryption Based on Deep Learning

作　　者：徐昭 周昕[1] 白星 李聪 陈洁 倪洋 XU Zhao;ZHOU Xin;BAI Xing;LI Cong;CHEN Jie;NI Yang(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电子信息学院,四川成都610065

出　　处：《光学与光电技术》2021年第3期75-81,共7页Optics & Optoelectronic Technology

基　　金：国家自然科学基金(61475104、61177009)资助项目。

摘　　要：混沌加密由于其初始值敏感性、伪随机性和运动轨迹的不可预测性而被广泛应用于图像加密领域。提出了一种通过深度学习来攻击Lorenz混沌加密系统的灰度图像重建方法,通过残差网络实现了对一系列明文-密文对数据集进行训练,从而拟合出密文到明文的过程,然后将训练好的网络应用在独立于训练集的密文上,恢复出与明文非常接近的图像。数值仿真结果验证了这种灰度图像重建方法的有效性。Chaotic encryption is widely used in the field of image encryption due to its initial value sensitivity,pseudorandomness,and unpredictability of motion trajectories. Deep learning(DL)as a method of machine learning was proposed for decades. With the development of computer’s performance,the practicality of deep learning has been proved more and more. It has achieved good resultsin many fields. In this paper,we propose to attack Lorenz chaotic encrypted system of grayscale images by the deep learning method via Residual Networks. After the training process to a series of input and output plaintext-ciphertext pairs,ResNet can fit the process from ciphertext to plaintext. We can finally recover the image approximate to the plaintext image accordingto the ciphertext which is independent to the original plaintext-ciphertext pairs set. Numerical simulation has verified that the result recovering from the chaotic encrypted system is very good.

关 键 词：混沌加密 灰度图像 深度学习 残差网络 图像重建 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]