Purpose–The purpose of this paper is to investigate two-dimensional outer totalistic cellular automata(2D-OTCA)rules other than the Game of Life rule for image scrambling.This paper presents a digital image scramblin...Purpose–The purpose of this paper is to investigate two-dimensional outer totalistic cellular automata(2D-OTCA)rules other than the Game of Life rule for image scrambling.This paper presents a digital image scrambling(DIS)technique based on 2D-OTCA for improving the scrambling degree.The comparison of scrambling performance and computational effort of proposed technique with existing CA-based image scrambling techniques is also presented.Design/methodology/approach–In this paper,a DIS technique based on 2D-OTCA with von Neumann neighborhood(NvN)is proposed.Effect of three important cellular automata(CA)parameters on gray difference degree(GDD)is analyzed:first the OTCA rules,afterwards two different boundary conditions and finally the number of CA generations(k)are tested.The authors selected a random sample of gray-scale images from the Berkeley Segmentation Data set and Benchmark,BSDS300(www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)for the experiments.Initially,the CA is setup with a random initial configuration and the GDD is computed by testing all OTCA rules,one by one,for CA generations ranging from 1 to 10.A subset of these tested rules produces high GDD values and shows positive correlation with the k values.Subsequently,this sample of rules is used with different boundary conditions and applied to the sample image data set to analyze the effect of these boundary conditions on GDD.Finally,in order to compare the scrambling performance of the proposed technique with the existing CA-based image scrambling techniques,the authors use same initial CA configuration,number of CA generations,k紏10,periodic boundary conditions and the same test images.Findings–The experimental results are evaluated and analyzed using GDD parameter and then compared with existing techniques.The technique results in better GDD values with 2D-OTCA rule 171 when compared with existing techniques.The CPU running time of the proposed algorithm is also considerably small as compared to existing techniques.Originality/value–In this paper,the authors focused on using von Neumann neighborhood(NvN)to evolve the CA for image scrambling.The use of NvN reduced the computational effort on one hand,and reduced the CA rule space to 1,024 as compared to about 2.62 lakh rule space available with Moore neighborhood(NM)on the other.The results of this paper are based on original analysis of the proposed work.展开更多
This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevale...This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.展开更多
文摘Purpose–The purpose of this paper is to investigate two-dimensional outer totalistic cellular automata(2D-OTCA)rules other than the Game of Life rule for image scrambling.This paper presents a digital image scrambling(DIS)technique based on 2D-OTCA for improving the scrambling degree.The comparison of scrambling performance and computational effort of proposed technique with existing CA-based image scrambling techniques is also presented.Design/methodology/approach–In this paper,a DIS technique based on 2D-OTCA with von Neumann neighborhood(NvN)is proposed.Effect of three important cellular automata(CA)parameters on gray difference degree(GDD)is analyzed:first the OTCA rules,afterwards two different boundary conditions and finally the number of CA generations(k)are tested.The authors selected a random sample of gray-scale images from the Berkeley Segmentation Data set and Benchmark,BSDS300(www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)for the experiments.Initially,the CA is setup with a random initial configuration and the GDD is computed by testing all OTCA rules,one by one,for CA generations ranging from 1 to 10.A subset of these tested rules produces high GDD values and shows positive correlation with the k values.Subsequently,this sample of rules is used with different boundary conditions and applied to the sample image data set to analyze the effect of these boundary conditions on GDD.Finally,in order to compare the scrambling performance of the proposed technique with the existing CA-based image scrambling techniques,the authors use same initial CA configuration,number of CA generations,k紏10,periodic boundary conditions and the same test images.Findings–The experimental results are evaluated and analyzed using GDD parameter and then compared with existing techniques.The technique results in better GDD values with 2D-OTCA rule 171 when compared with existing techniques.The CPU running time of the proposed algorithm is also considerably small as compared to existing techniques.Originality/value–In this paper,the authors focused on using von Neumann neighborhood(NvN)to evolve the CA for image scrambling.The use of NvN reduced the computational effort on one hand,and reduced the CA rule space to 1,024 as compared to about 2.62 lakh rule space available with Moore neighborhood(NM)on the other.The results of this paper are based on original analysis of the proposed work.
基金Türkiye Bilimsel ve Teknolojik Arastırma Kurumu。
文摘This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.
