Robust Image Encryption With Scanning Technology, the El-Gamal Algorithm and Chaos Theory

Abstract

Digital images are the most frequently used signals to convey information in the internet era. The security of these images is the primary concern in rapidly changing networked environments. In this research, we present a novel approach to secure images by integrating a scanning technique, the El-Gamal public key cryptosystem and chaotic systems. In brief, zigzag and spiral scanning are used first to construct a permuted image. Then, the El-Gamal encryption algorithm is exploited to encrypt the permuted image. Finally, Lorenz and Rössler chaotic sequences are utilized to scramble the pixel locations in the confusion and diffusion stages. This last step that mixes two stages can fortify the entire security performance and enlarge the key size. Exhaustive analysis has been carried out on the SIPI (signal and image processing institute) dataset to assess the efficiency and security of the proposed method. Numerical and visual results indicate the capability of the proposed image cryptosystem to protect images against several known attacks. In addition, the comparative analysis results indicate that the proposed approach outperforms the compared approaches in terms of the visual quality and security criteria.INDEX TERMS Image security, scanning, chaotic, Lorenz and Rössler sequences, El-Gamal algorithm.

Review of Image Security approaches: Concepts, Issues, Challenges and Applications

“…This algorithm has the correlation coefficient between adjacent pixels close to zero, and has random and uniform statistical characteristics in the encrypted image, and the algorithm has good ability to resist differential attacks and known/chosen plaintext attacks. Sura F. Yousif, Ali J. Abboud, and Hussein Y. Radhi [5] have introduced an image security approach to secure digital images by utilizing a scan mechanism, the El-Gamal asymmetric key cryptosystem and chaotic systems. The Lorenz chaotic system is adopted for the resulting permutated images to confuse the relation between the grey image pixels by changing their locations, whereas the Rössler chaotic system is applied to the obtained confused images to diffuse the image pixels through varying their values to achieve a second layer of encryption.…”

“…• NPCR [1,[4][5][6][7][8][9][10][11][12][13][14][15][17][18][19]: For better decryption algorithm NPCR value should be low. • UACI [1,[4][5][6][7][8][9][10][11][12][13][14][15][17][18][19]: For good decrypted image quality UACI value should be low. • PSNR [5,6,10,13,14,17,18]: The larger the PSNR value, the more similar the decrypted image is to the original image.…”

“…4. Correlation Coefficients [1,[4][5][6][7][8][9][10][11][12][13][14][15][17][18][19]: For a plain image, each pixel is highly correlated with its neighbor and adjacent pixels either in horizontal, vertical or diagonal direction. However, an efficient image cryptosystem should produce the cipher image with sufficiently low correlation (close to zero) in the adjacent pixels.…”

“…8. Encryption Efficiency: Peak signal to noise ratio (PSNR) [5,6,10,13,14,17,18] is used to measure the difference between the original image and the encrypted image. Also, the PSNR is usually used to assess the performance of image reconstruction.…”

“…10. Time Complexity (or Time Analysis or Computational Complexity) [5,7,10,14,18]: Estimate the time complexity in each step of the encryption process to evaluate proposed algorithm's total time complexity. 11.…”