吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (3): 1060-1066.doi: 10.13229/j.cnki.jdxbgxb20200521
• 计算机科学与技术 • 上一篇
Jin-qing LI1,2(),Jian ZHOU1,2,Xiao-qiang DI1,2,3
摘要:
为克服光学图像加密方法受光学器件性能限制和随机相位掩膜板制造工艺复杂的问题,提出了一种基于循环生成对抗网络(CycleGAN)的学习型光学图像加密方案。首先,使用经典双随机相位编码加密明文样本图像,构造出明文图像-密文图像训练集。然后,将其作为循环生成对抗网络的输入,自动学习光学图像加密的加密特性,训练得到光学图像加密学习模型。最后,利用仿真实验对使用CycleGAN训练的加密模型生成的图像进行加密解密性能测试。数据分析表明,该模型能够有效保护图像信息的安全和较好地恢复密文图像,学习型光学加密模型具有加密性能不受光学加密器件限制的优点,可以实现批量图像的快速加密。
中图分类号:
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