No abstract

No abstract

No abstract

To mitigate the shortcomings of existing medical image encryption algorithms, including a lack of anti-tampering methods and security, this report presents an anti-tampering encryption algorithm for medical images that is based on a self-verification matrix. First, chaotic coordinates generated by chaos are used to traverse all pixels in a plain image to generate a two-dimensional matrix (a self-verification matrix) with positioning information. The accurate location of illegally altered image pixels can be detected using the self-verification matrix. To improve the security of the self-authentication matrix, DNA coding is also applied to the self-authentication matrix, and the plain image is also diffused statically to destroy the pixel distribution. Next, the scrambled image and self-verification matrix are mixed and cross-scrambled. Finally, the fused image is diffused dynamically to improve the security of the encrypted image. Experimental simulation and performance analysis show that the algorithm achieves good encryption effectiveness, provides strong anti-tampering capabilities, and can accurately locate at least 4 pixels.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.