Optimal Stream Encryption for Multiple Shares of Images by Improved Cuckoo Search Model

Shankar, K.; Elhoseny, Mohamed

doi:10.1007/978-3-030-20816-5_10

Cited by 1 publication

(1 citation statement)

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“…However, because of significant computational resource overheads, WSN as encryption instruments may be impracticable. Shankar and Elhoseny (2019b) proposed Discrete Wavelet Transform implemented on images to produce sub-images. In this, multiple shadows are constructed and then encrypted and decrypted using Homomorphic Encryption and for the generation of optimal key, Oppositional based Harmony Search algorithm is used.…”

Section: Related Workmentioning

confidence: 99%

Primary Color Based Numerous Image Creation in Visual Cryptography with the Help of Grasshopper Algorithm, Artificial Neural Network and Elliptic Curve Cryptography

Goon¹,

Pal²,

Dihidar³

2022

Journal of Computer Science

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The two primary restrictions of visual cryptography methods, contract, and security are well-known. Visual cryptography is considered secure if no share reveals information about the secure picture and this image is unreachable when shares are combined. Recursion can be used in a visual cryptography method to ensure the integrity of both arguments. Sometimes, the many shares are generated illogically in such a manner that they sometimes reveal the actual image. The model proposed in the paper has been proved beneficial in reducing the computation, noise, or distortion in the recreated image and helps in providing amended contracts. The suggested scheme uses unique pixel patterns to create a contract for the decrypted image. The contract for the recursion visual cryptography scheme is alterable from the white pixel pattern to new pixel patterns. The elliptic curve cryptography approach is used to ensure the privacy and security of the image. This study has taken a four-share mechanism in which each component is divided into four shares for each color segment. The proposed algorithm uses the Grasshopper algorithm to select the bit's positions where the encryption must be done. Neural Networks have been used as a cross validator in the proposed work model. A new fitness function is designed for the Grasshopper algorithm. The proposed algorithm is compared based on Mean Square Error and Peak Signal to Noise Ratio.

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Section: Related Workmentioning

confidence: 99%