Medical Image Encryption and Compression Scheme Using Compressive Sensing and Pixel Swapping Based Permutation Approach

Abstract

This paper presents a solution to satisfy the increasing requirements for secure medical image transmission and storage over public networks. The proposed scheme can simultaneously encrypt and compress the medical image using compressive sensing (CS) and pixel swapping based permutation approach. In the CS phase, the plain image is compressed and encrypted by chaos-based Bernoulli measurement matrix, which is generated under the control of the introduced Chebyshev map. The quantized measurements are then encrypted by permutation-diffusion type chaotic cipher for the second level protection. Simulations and extensive security analyses have been performed. The results demonstrate that at a large scale of compression ratio the proposed cryptosystem can provide satisfactory security level and reconstruction quality.

A Novel 1D Hybrid Chaotic Map-Based Image Compression and Encryption Using Compressed Sensing and Fibonacci-Lucas Transform

“…It is clear from Figure 6 that our scheme could achieve satisfactory recovery performance. Further, the methods with original Bernoulli measurement matrix and the one generated by Chebyshev chaotic map in [27] are also listed for the purpose of comparison with the one generated by the proposed chaotic map. Obviously, the recovery performance by our chaotic map almost approaches the one by Bernoulli random matrix and exceeds the one by Chebyshev chaotic map, whereas our method could achieve higher transmission efficiency in contrast to the original Bernoulli and has larger key space compared to Chebyshev chaotic map.…”

“…Recently, some researchers proposed a kind of compression-combined encryption method based on compressive sensing (CS) [21][22][23][24][25][26][27]. CS includes sparse representation, linear measurement, and reconstruction processes.…”

“…These CSbased image encryption methods could achieve satisfactory security performance except for the resistance of differentia attack due to the absence of diffusion process. Zhang et al [27] apply the encryption and compression scheme into the field of medical images. The novelties of their work lie in that Bernoulli measurement matrix in CS is constructed by using Chebyshev map and quantized measurements by max-Llyod are encrypted in diffusion phase.…”

Cellular Neural Network-Based Medical Image Encryption

“…Hence, an good cryptosystem should produce the ciphertexts with weak correlation among adjacent pixels. The correlation coefficient (CC) between adjacent pixels [19] can be computed using the Eq. (8):…”

“…In this regard, diverse encryption techniques have been proposed based on genetic algorithms [11,12], combination of encryption and water marking approach [13], cosine number transform (CNT) [14], chaotic maps etc. The potent properties such as erratic behavior, sensitive dependence to system parameters and initial conditions, high level of security while still maintaining the simplicity allows the chaos based algorithms to provide an efficient way to encrypt multimedia based data [15][16][17][18][19][20]. Hence, researchers have focused to design encryption algorithms which provide high level security using the dynamic properties of chaotic systems since 1990s.…”