Asymmetric Optical Image Encryption With Silhouette Removal Using Interference and Equal Modulus Decomposition

Luan, Guangyu; Li, Aichuan; Chen, Zhengguang; Huang, Caojun

doi:10.1109/jphot.2020.2963921

Cited by 19 publications

(6 citation statements)

References 29 publications

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“…Image matching is a tool used either to describe the entire image with global features which can describe a complete picture by only one vector or to concentrate on important image information that is stronger. We must deal with the contents and obtain information from these two methods in order to implement the two, which means that both methods take time to handle images and that they require hardware capable of acquiring these features efficiently [26]. This leads us to the fact that mobile device extraction takes a lot of time, random memory, thus increasing battery consumption, thus affecting the user's everyday use.…”

Section: Figure 1 Image Deduplication Processmentioning

confidence: 99%

An Image Decompression Model with Reversible Pixel Interchange Decryption Model Using Data Deduplication

Manikyam¹,

Devi²

2022

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The images may or may not be confidential if communications occur via images. But it becomes complicated when we want to convey a picture, which only the sender and the recipient must know. Since data that have been sent during transmission can be lost or an individual may hack and misuse this picture. Safety of the data is important in such scenarios. In order to reduce storage space and costs, imaging deduplication (DD) technology is proposed. The concept of convergent encryption and decryption was suggested to protect the confidentiality of the image. The deduplication scheme encrypts/recodes an image with a convergent encryption/decryption key obtained from computing the hash value of the content of the image. Implementing DD over encrypted/decrypted data is a major challenge to optimize storage efficiently in a highly secured way in an integrated storage and computer environment. The original image is segmented into blocks of same size during the initial step, and sub classification is performed for accurate image extraction within the limits. The pixels of neighboring sub blocks are swapped using a random matrix. Following that, each pixel is randomly exchanged for neighboring blocks using a random matrix, and each block is encrypted using the suggested function before being sent to the receiver. In this manuscript, an Image Decompression Model with Reversible Pixel Interchange Decryption model using Data Deduplication (IDRPID-DD) is introduced that provides security during storage and data transmission. The proposed model is compared with the traditional methods and the results show that the proposed model deduplication identification and eradication levels are high and the proposed decryption model is strong.

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Section: Figure 1 Image Deduplication Processmentioning

confidence: 99%

An Image Decompression Model with Reversible Pixel Interchange Decryption Model Using Data Deduplication

Manikyam¹,

Devi²

2022

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“…Based on DRPE scheme, optical cryptosystem was further studied and extended from the Fourier domain to different transform domains [2][3][4][5]. Meanwhile, phase truncation scheme [6][7], diffraction scheme [8][9], and interference scheme [10][11][12] for optical cryptosystems were proposed based on different optical techniques. Optical cryptosystem can transform the image information into the ciphertext with noise-like white spectrum.…”

Section: Introductionmentioning

confidence: 99%

Incoherent optical cryptosystem based on hybrid wave aberration

Ji,

Chang,

Yang

et al. 2023

Optical Design and Testing XIII

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An incoherent optical cryptosystem based on hybrid wave aberration is proposed in this study. The proposed cryptosystem is designed with different channels to divide the object. In each channel, different wave aberrations are allocated to form the hybrid wave aberration. When the object is imaged by the channel with huge wave aberrations, its corresponding image will be blurry enough to form a ciphertext. Because the different wave aberrations in different channels can be regarded as the key, the proposed optical cryptosystem can achieve multi-key encryption as well as local encryption. The proposed optical cryptosystem enhances the security of incoherent optical cryptosystem through the quantity of keys and the special construction method of ciphertext.

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“…In 1995, Refregier et al [ 3 ] proposed an optical image encryption scheme based on double random phase encoding for the first time, which means it uses two uncorrelated random phase templates and Fourier transform to realize optical image encryption. In essence, this scheme is based on optical transformation to disturb the information of plaintext images and generate ciphertext images, such as fractional Fourier transform [ 4 ], fractional wavelet transform [ 5 ], fractional Merlin transform [ 6 ], interference [ 7 ], single-pixel imaging [ 8 , 9 ], etc. Optical image encryption belongs to parallel encryption with high speed and high efficiency, but its encryption performance is limited by the technology and precision of various optical devices in the optical path [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning

Meng

Yin

et al. 2021

Sensors

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Multi-image encryption technology is a vital branch of optical encryption technology. The traditional encryption method can only encrypt a small number of images, which greatly restricts its application in practice. In this paper, a new multi-image encryption method based on sinusoidal stripe coding frequency multiplexing and deep learning is proposed to realize the encryption of a greater number of images. In the process of encryption, several images are grouped, and each image in each group is first encoded with a random matrix and then modulated with a specific sinusoidal stripe; therefore, the dominant frequency of each group of images can be separated in the Fourier frequency domain. Each group is superimposed and scrambled to generate the final ciphertext. In the process of decryption, deep learning is used to improve the quality of decrypted image and the decryption speed. Specifically, the obtained ciphertext can be sent into the trained neural network and then the plaintext image can be reconstructed directly. Experimental analysis shows that when 32 images are encrypted, the CC of the decrypted result can reach more than 0.99. The efficiency of the proposed encryption method is proved in terms of histogram analysis, adjacent pixels correlation analysis, anti-noise attack analysis and resistance to occlusion attacks analysis. The encryption method has the advantages of large amount of information, good robustness and fast decryption speed.

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Asymmetric Optical Image Encryption With Silhouette Removal Using Interference and Equal Modulus Decomposition

Cited by 19 publications

References 29 publications

An Image Decompression Model with Reversible Pixel Interchange Decryption Model Using Data Deduplication

An Image Decompression Model with Reversible Pixel Interchange Decryption Model Using Data Deduplication

Incoherent optical cryptosystem based on hybrid wave aberration

A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning

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