In this paper, we aimed to discuss the security authentication requirements of medical images in the medical network, and a security authentication method is designed based on fractal and visual cryptography. Based on the discrete fractal Brownian random field model, the gray-level statistical information and spatial structure information of medical images is fully mined. The gray distribution of medical images is expressed in the form of fractal features. By using the spatial data mining methods, the data of fractal structure space is analyzed, and by using the stability of the energy structure, the authentication features are formed. Using the visual cryptography (VC), the robustness of the authentication method is further enhanced. Through the centralized test of common medical images and the comparison analysis with existing methods, it is further verified that the method is effective against common attacks such as JPEG compression, scaling, rotation operation, clipping, added noise, filtering, and blurring.

中文翻译：

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基于分形布朗模型和视觉密码学的医学图像鲁棒认证算法

在本文中，我们旨在讨论医疗网络中医学图像的安全认证要求，并设计了一种基于分形和视觉密码学的安全认证方法。基于离散分形布朗随机场模型，充分挖掘医学图像的灰度统计信息和空间结构信息。医学图像的灰度分布以分形特征的形式表示。利用空间数据挖掘方法，对分形结构空间的数据进行分析，利用能量结构的稳定性，形成认证特征。使用可视密码术 (VC)，进一步增强了身份验证方法的鲁棒性。通过对常见医学影像的集中测试和与现有方法的对比分析，