An Encryption Algorithm Based on Optical Chaos and Image Quotient and Residue Preprocessing
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摘要: 随着现代科学技术的发展,人们对图像信息传输的安全性要求越来越高,以混沌理论为基础的图像加密方案更加受到重视。该文提出一种新型的光混沌图像加密传输系统以及图像“自加密”算法,该系统的主激光器(ML)经全光反馈后分别注入到3个半导体激光器(SLs)中,从而产生3个同步的混沌序列。在图像加密之前,先对明文图像进行预处理,得到两幅图像,一幅是明文图像取商的图像,另一幅是明文图像取余的图像。利用发送端的混沌序列对预处理的两幅图像进行多次加密、隐写以及扩散等操作,得到密文图像。实验结果表明:该文的密文图像像素值分布均匀,各像素间的相关性被打破,NPCR和UACI均接近理想值。该图像预处理的方法能够有效地使图像像素值更为集中、分布更加均匀,同时结合光混沌对图像进行加密,大大地提高传输图像的安全性。Abstract: With the development of modern science and technology, people have higher and higher requirements for the security of image information transmission, and the image encryption scheme based on chaos theory has attracted more and more attention. In this paper, a novel optical chaotic image encryption transmission system and a “self-encryption” algorithm for images are proposed and demonstrated. The Master Laser (ML) of the system is injected into the other three Semiconductor Lasers (SLs) respectively after full-optical feedback, then three synchronous chaotic sequences are generated. Before encrypting the image, the plaintext image is preprocessed, and two images are obtained, one is the image after the quotient of the plain image, the other is the image after the redundancy of the plain image. The chaotic sequence of the sender is used to encrypt, steganograph and spread the two preprocessed images for many times, and then the ciphertext image is obtained. The experimental results show that the pixel values of the ciphertext images obtained in this paper are evenly distributed, the correlation between each pixel is broken, and both NPCR and UACI are close to the ideal value. The image preprocessing method proposed in this paper can effectively make the image pixel value more concentrated, more uniform distribution. Combining with the optical chaos to encrypt the image, it greatly improves the security of the transmitted image.
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Key words:
- Semiconductor Laser (SL) /
- Optical chaos /
- Image encryption /
- Image processing
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表 1 激光器的参数
参数 数值 参数 数值 $ \partial $ 3 $ {C} $ 3×108 $ {{K}_{f}} $ 30 ns $ {g} $ 1.2×10–5 $ {\tau _{f}} $ 1.2 ns $ {{N}_0} $ 1.25×108 $ {{K}_{{inj}}} $ 50 $ {s} $ 5×10–7 ${{e} }$ 1.6×10–19 $ \gamma $ 496 $ {\gamma _{e}} $ 0.65 表 2 直方图检测
图像 $ \chi _{{\text{test}}}^2 $ 结果 明文Lena 30665.7031 不通过 密文Lena 243.7188 通过 明文Baboo 42256.0859 不通过 密文Baboo 232.8672 通过 明文Boat 100313.132 不通过 密文Boat 226.4297 通过 明文Camera 109020.992 不通过 密文Camera 247.4375 通过 表 3 相关性检测
表 4 不同图像的NPCR和UACI
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