2D 3D Image Encryption via Structural Anisotropy
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Solution Overview
Problem
Current image encryption/decryption methods are inefficient and time-consuming, particularly for high-resolution images with structural anisotropy, as they fail to effectively quantify and compare anisotropic structures across different objects and do not provide tools for secure transmission and reconstruction of original images from structural features.
Innovation Solution
The system employs a set of transects to convert binary images into N-partite graphs and tables, allowing for secure transmission and reconstruction with the use of a one-time secret key, and calculates indices of intrinsic anisotropy to verify image integrity, enabling secure and efficient encryption/decryption of 2-D and 3-D arbitrary images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional pixel-based encryption methods are used, then implementation simplicity is maintained, but encryption time and computational complexity increase significantly for high-resolution images
Solution Approach 1:
The patent segments the image into structural features (edges, corners, regions) rather than processing every pixel individually. By dividing the image into meaningful structural components and applying encryption to these segments, the system reduces computational complexity while maintaining security, directly addressing the contradiction between encryption speed and processing complexity
Solution Approach 2:
The invention extracts key structural information from the image (such as transect data and structural parameters) and uses this extracted information for encryption. By taking out only the essential structural features rather than encrypting the complete pixel data, the system achieves faster encryption without sacrificing image quality or security
2Reliability
If image encryption is applied to ensure secure transmission, then security is improved, but the ability to verify image integrity and authenticity deteriorates
Solution Approach 1:
The patent incorporates hash value calculation as a feedback mechanism that verifies image integrity after encryption. By computing and comparing hash values of the encrypted image with expected values, the system provides feedback on whether the image was tampered with during transmission, simultaneously ensuring security and maintaining integrity verification capability
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing hash values or structural parameters before encryption. These pre-computed values serve as reference points that can be used to verify image integrity after transmission, allowing the system to maintain both security and verification precision
3Measurement precision
If arbitrary images with structural anisotropy are processed, then measurement accuracy is improved, but the difficulty of quantifying and comparing anisotropic structures increases
Solution Approach 1:
The patent applies local quality analysis by examining different regions and structural features of the image individually. By analyzing local structural characteristics (such as transect orientations and local anisotropy measures) rather than treating the entire image uniformly, the system achieves accurate quantification of anisotropic structures while making the measurement process more manageable through localized analysis
Solution Approach 2:
The invention transforms the complex 2D image data into a simplified representation using transects and structural parameters. By converting the image into a set of 1D transect measurements and associated parameters, the system reduces the dimensionality of the data while preserving essential structural information, thereby simplifying the quantification and comparison of anisotropic features
Data Source
AI summary
An image encryption/decryption system performs quantification and reconstruction of 2-D and 3-D layered and arbitrary grayscale or binary images in raster or vector format and supports their secure transmission between a Sender and a Receiver. Quantification parameters of the images include Indices of Intrinsic Anisotropy of size (IIAsize) and structure (IIAstr), which reflect the image anisotropy with high sensitivity. The IIASTR and IIASIZE are used as hash values in authentication of a transmitted image. The system assures that the same set of transects which was used by the Sender in the image encryption will be used only by an authorized Receiver for decryption of the image. Using the Quantification parameters (IIAstr and IIAsize), the present technique pinpoints the differences in structure and size in time series images, computes the localization of the differences between images of different categories and presented in different formats, and is capable of performing reconstruction of original 2-D and 3-D images from their N-partite graphs G (N) and F(M,N) table. The subject system and method may be used for fully automated quantification of dimensions and structure of 2-D and 3-D lesions with fuzzy margins in a precise and highly efficient manner.