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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. [ABSTRACT FROM AUTHOR]

A bit‐level image encryption algorithm based on Fully‐Connected‐Like network(FCLN) and random modification of edge pixels is proposed. In the paper, in order to enhance the security of the cryptographic system, random noise is first used to modify the least significant bits of the edge pixels of the image, and the modified image is used as the input image. Later,the chaotic sequence is used to perform cyclic shift transformation on the image. In the subsequent steps, the FCLN is generated based on a fully connected neural network, which can perform scrambling and diffusion operations on the input image. Finally, the bidirectional diffusion method is used to diffuse the image forward and backward. In addition, the image after the edge pixel modification is convolved with the chaotic sequence, and the initial value of the chaotic system is set by the result to establish the correlation between the plain image and the algorithm, which makes the algorithm resistant to known/chosen plaintext attack. Experimental results show that although the image is modified by random noise, the decrypted image is visually the same as the original image. At the same time, through the analysis of common attacks such as differential attacks, noise attacks, and data loss attacks, our algorithm shows high security. [ABSTRACT FROM AUTHOR]