引言AVL(Audio Video Light)中央控制系统作为展台搭建的核心控制枢纽,针对系统构成、技术特点、集成策略以及应用实践等多个维度展开深入探讨。研究聚焦于系统在展览环境中的需求分析、设备选型与系统设计等关键环节,阐述了场景化应用...引言AVL(Audio Video Light)中央控制系统作为展台搭建的核心控制枢纽,针对系统构成、技术特点、集成策略以及应用实践等多个维度展开深入探讨。研究聚焦于系统在展览环境中的需求分析、设备选型与系统设计等关键环节,阐述了场景化应用、互动体验增强以及数据监测分析等具体实施方案。展开更多
Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data.This property makes it difficult for steganalysts’powerful data extrac...Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data.This property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation.However,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations.In addition,these numbers may cluster in certain ranges.The hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can detect.Therefore,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image.This paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data concealment.The AVL tree algorithm provides several advantages for image steganography.Firstly,it ensures balanced tree structures,which leads to efficient data retrieval and insertion operations.Secondly,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image quality.The data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel extraction.The green channel serves as the foundation for building a balanced binary tree.First,the sender identifies the colored cover image and secret data.The sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated information.The next step is to create a balanced binary tree based on the green channel.Utilizing the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue channel.The first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret data.After embedding the bits in the binary tree level by level,the model restores the AVL tree to create the stego image.Ultimately,the receiver receives this stego image through the public channel,enabling secret data recovery without stego or crypto keys.This method ensures that the stego image appears unsuspicious to potential attackers.Without an extraction algorithm,a third party cannot extract the original secret information from an intercepted stego image.Experimental results showed high levels of imperceptibility and security.展开更多
针对传统AVL(Adelson-Velskii and Landis)树重平衡算法代码量大、流程复杂、调整率过高的问题,提出一种统一重平衡算法,并提出广义AVL树的概念。统一重平衡算法能对AVL树的失衡节点进行自动分类、调整,取消了传统重平衡方法中的四种旋...针对传统AVL(Adelson-Velskii and Landis)树重平衡算法代码量大、流程复杂、调整率过高的问题,提出一种统一重平衡算法,并提出广义AVL树的概念。统一重平衡算法能对AVL树的失衡节点进行自动分类、调整,取消了传统重平衡方法中的四种旋转操作。广义AVL树放松了AVL树的平衡约束,允许左右子树树高相差不超过N(N≥1),当更新操作(插入/删除)执行后,广义AVL树只在平衡约束条件不满足时采用统一重平衡算法进行调整。理论分析与实验结果表明,广义AVL树的调整率随着N的增大而显著降低:N为5时,调整率低于4%;N为13时调整率低于千分之一。广义AVL树的调整率远低于红黑树等经典数据结构,适合并发应用。展开更多
文摘Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data.This property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation.However,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations.In addition,these numbers may cluster in certain ranges.The hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can detect.Therefore,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image.This paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data concealment.The AVL tree algorithm provides several advantages for image steganography.Firstly,it ensures balanced tree structures,which leads to efficient data retrieval and insertion operations.Secondly,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image quality.The data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel extraction.The green channel serves as the foundation for building a balanced binary tree.First,the sender identifies the colored cover image and secret data.The sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated information.The next step is to create a balanced binary tree based on the green channel.Utilizing the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue channel.The first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret data.After embedding the bits in the binary tree level by level,the model restores the AVL tree to create the stego image.Ultimately,the receiver receives this stego image through the public channel,enabling secret data recovery without stego or crypto keys.This method ensures that the stego image appears unsuspicious to potential attackers.Without an extraction algorithm,a third party cannot extract the original secret information from an intercepted stego image.Experimental results showed high levels of imperceptibility and security.
文摘针对传统AVL(Adelson-Velskii and Landis)树重平衡算法代码量大、流程复杂、调整率过高的问题,提出一种统一重平衡算法,并提出广义AVL树的概念。统一重平衡算法能对AVL树的失衡节点进行自动分类、调整,取消了传统重平衡方法中的四种旋转操作。广义AVL树放松了AVL树的平衡约束,允许左右子树树高相差不超过N(N≥1),当更新操作(插入/删除)执行后,广义AVL树只在平衡约束条件不满足时采用统一重平衡算法进行调整。理论分析与实验结果表明,广义AVL树的调整率随着N的增大而显著降低:N为5时,调整率低于4%;N为13时调整率低于千分之一。广义AVL树的调整率远低于红黑树等经典数据结构,适合并发应用。
