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中国科学院大学学报 ›› 2016, Vol. 33 ›› Issue (5): 604-611.DOI: 10.7523/j.issn.2095-6134.2016.05.005

• 数学与物理学 • 上一篇    下一篇

双随机相位加密系统的无约束最优化攻击

王国华1,2, 李拓1,3, 张三国1,2, 史祎诗1,3   

  1. 1 中国科学院大学, 北京 100049;
    2 中国科学院大数据挖掘与知识管理重点实验室, 北京 100049;
    3 中国科学院光电研究院, 北京 100094
  • 收稿日期:2016-02-24 修回日期:2016-03-31 发布日期:2016-09-15
  • 通讯作者: 史祎诗
  • 基金资助:

    国家自然科学基金(61575197)和中国科学院科学融合教育创新项目资助

Unconstrained optimization attack on double random phase cryptosystem

WANG Guohua1,2, LI Tuo1,3, ZHANG Sanguo1,2, SHI Yishi1,3   

  1. 1 University of Chinese Academy of Sciences, Beijing 100049, China;
    2 Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100049, China;
    3 Academy of Opto-electronics, Chinese Academy of Sciences, Beijing 100094, China
  • Received:2016-02-24 Revised:2016-03-31 Published:2016-09-15

摘要:

提出一种针对双随机光学相位加密系统的无约束最优化攻击算法.在已知明文条件下,首次将双随机相位加密系统的攻击问题转化为一个单目标无约束最优化模型.基于该模型,在相应的攻击算法设计中,采用拟牛顿矩阵代替Hessian矩阵以准确获取系统的密钥,避免传统牛顿法需要计算Hessian矩阵的逆等严重缺陷.同时,因有效利用拟牛顿矩阵的正定、对称、可迭代求逆的特点,新的攻击算法具有恢复效果好、收敛速度快、初值依赖弱、鲁棒性较强等优势.此外,本算法所需约束条件较少,可方便地移植到其他光学加密系统的攻击中.

关键词: 光学信息安全, 双随机相位加密系统, 光学攻击, 非约束最优化

Abstract:

An unconstrained optimization method is proposed to attack the double phase encryption system.Under the condition of knowing the plaintext, the new attack method builds an unconstrained optimization model and gets the accurate phase key via this model. Using the acquired phase key, the attacker decrypts the followed cipher. The new attack method transforms the problem of attacking the double phase encryption system into an unconstrained optimization model. The new attack method replaces Hessian matrix by quasi-Newton matrix to avoid computation of the reverse of Hessian matrix. The new attack method has fast convergence speed and strong robustness, and it is not too sensitive to the original values of the variables. This attack method can be applied to other encryption systems.

Key words: optical information security, double random phase cryptosystem, optical attack, unconstrained optimization

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