Multi-Scale Compressed Sensing Image Encryption with Markov Model
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Solution Overview
Problem
Existing digital image encryption methods face challenges such as ease of access to original image information, poor plaintext sensitivity, key sensitivity, and poor reconstruction quality of decrypted images, necessitating a more secure and effective encryption method.
Innovation Solution
An image encryption method based on multi-scale compressed sensing and a Markov model, which generates chaotic sequences, performs discrete wavelet transforms, and uses state transition probability matrices for scrambling and diffusion to enhance encryption and decryption processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional digital image encryption methods are used, then encryption can be performed, but original image information can still be accessed easily and plaintext sensitivity is poor
Solution Approach 1:
The image is divided into multiple non-overlapping blocks, and each block is encrypted independently through compressed sensing. This segmentation prevents attackers from accessing meaningful information about the original image structure, as each block's encryption is independent and the reconstruction requires all blocks to be properly decrypted and assembled.
Solution Approach 2:
The patent changes the parameter of sampling rate to different scales (multi-scale compressed sensing). By using different sampling rates at different scales, the encryption becomes more complex and sensitive to plaintext changes, making it difficult for attackers to predict or reverse-engineer the original image information.
2Reliability
If traditional image encryption schemes are applied, then encryption is achieved, but key sensitivity and plaintext sensitivity are poor
Solution Approach 1:
The patent incorporates feedback mechanisms where the encryption process uses the plaintext image information itself to generate encryption parameters (such as chaotic sequences and measurement matrices). This creates strong key sensitivity because any change in the plaintext or key will fundamentally alter the encryption parameters, making the cipher highly sensitive to its inputs.
3Reliability
If conventional encryption methods are used, then encryption can be performed, but decrypted image reconstruction quality is poor
Solution Approach 1:
The patent employs dynamic scaling factors and adaptive parameter selection in the compressed sensing reconstruction process. The reconstruction algorithm dynamically adjusts parameters based on the encrypted data characteristics, enabling high-quality image recovery while maintaining strong encryption. This dynamic approach allows the system to optimize reconstruction quality for each specific encryption instance.
Data Source
AI summary
The present disclosure discloses an image encryption method based on multi-scale compressed sensing and a Markov model. According to the difference in information carried by low-frequency coefficients and high-frequency coefficients of an image, different sampling rates are set for the low-frequency coefficients and the high-frequency coefficients of the image, which can effectively improve the reconstruction quality of a decrypted image. The decrypted image obtained by the present disclosure has higher quality than the decrypted image generated by the existing scheme, and a better visual effect and more complete original image information can be obtained.