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Cryptanalysis of an Image Encryption Scheme Using Variant Hill Cipher and Chaos

EXPERT SYSTEMS WITH APPLICATIONS(2024)

Cited 26|Views12
Abstract
In 2019, a chaotic image encryption scheme based on a variant of the Hill cipher (VHC-CIES) was proposed by the Moroccan scholars. VHC-CIES introduces a Hill cipher variant and three improved one-dimensional chaotic maps to enhance the security. In this paper, we conduct a comprehensive cryptanalysis, and find that VHC-CIES can resist neither chosen-plaintext attack nor chosen-ciphertext attack due to its inherent flaws. When it comes to chosen-plaintext attack, firstly, we select a plaintext with the pixel values are all 0 and its corresponding ciphertext, and then use algebraic analysis to obtain the equivalent key stream for cracking VHC-CIES. Secondly, we select a plaintext which the pixel values are invariably 1 and obtain its corresponding ciphertext to obtain some Hill cipher variant parameters of VHC-CIES. Finally, we use the resulting steps of the first two to recover the original plain image from a given target cipher image. Similarly, a chosen-ciphertext attack method can also break VHC-CIES. Theoretical analysis and experimental results show that both chosen-plaintext attack and chosen-ciphertext attack can effectively crack VHC-CIES with data complexity of only O(2). For color images of size 256×256×3, when our simulation encryption time is 0.3150 s, the time for complete breaking by chosen-plaintext attack and chosen-ciphertext attack is about 0.6020 s and 0.9643 s, respectively. To improve its security, some suggestions for further improvement are also given. The cryptanalysis work in this paper may provide some reference for the security enhancement of chaos-based image cryptosystem design.
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Key words
Image encryption,Cryptanalysis,Chosen-plaintext attack,Chosen-ciphertext attack
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要点】:本研究通过结合体素形态学(VBM)和张力形态学(TBM)以及机器学习技术,识别颈椎病性脊髓病(CSM)患者的大脑特征性损伤,提高了CSM的精确诊断。

方法】:研究使用支持向量机(SVM)对57名CSM患者和57名健康对照的灰质和白质结构特征进行分类。

实验】:通过回顾性研究,对患者的灰质和白质体积变化进行VBM和TBM分析,结果显示CSM患者在多个大脑区域表现出特征性结构异常,VBM和TBM结合的准确分类达到81.58%,曲线下面积(AUC)为0.85。