Quantum Block Image Encryption Based on Arnold Transform and Sine Chaotification Model

Abstract

Quantum image encryption is an emerging technology for efficiently protecting visual information. A quantum block image encryption scheme is designed based on quantum Arnold transform (QArT) and lately proposed sine chaotification model (SCM) in this paper. First, in order to flexibly manipulate image blocks, a quantum block image representation (QBIR) model for block image is proposed, which encodes pixel gray values and position information of image blocks into two entangled qubit sequences. Then, QArT is applied to scramble the positions of image blocks. The final ciphertext image is obtained by quantum xor operations, which is completed with a pseudorandom sequence generated from SCM. The introduction of SCM dramatically enlarges the key space to resist brute-force attack. Moreover, the generated sequence is dependent on the original image to resist the chosen-plaintext attack. The main quantum circuits are given and the numerical simulation results demonstrate that the proposed quantum image encryption scheme is valid and reliable for quantum image protection in terms of security and computational complexity. INDEX TERMS Quantum image representation, quantum image encryption, quantum Arnold transform, sine chaotification model, XOR operation. XINGBIN LIU received the Ph.D. degree from the School of Information and Electronics, Beijing Institute of Technology, in 2017. He is currently a Postdoctoral Researcher with the College of Computer Science, Chongqing University. His main research interests include image security, image fusion, and quantum information processing.

A Quantum Image Encryption Method Based on DNACNot

“…The set of bits for each pixel value of an image corresponding to a specific bit position is called a bit plane [11,12]. For example, if the pixel value range for an image is [0,255], then the binary form represents it as 8-bit data; hence, the image has eight bit planes.…”

“…Almost all of these methods are designed for traditional digital images, while the encryption of quantum images remains in its infancy. However, as the traditional computer approaches its physical limits, researchers are beginning to study a new computing model, and quantum computers have produced a series of satisfactory encryption results [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. For example, EI-Latif [11] presented an encryption algorithm of quantum medical images.…”

“…The proposed algorithm utilizes gray code and a chaotic map. Liu [12] proposed quantum block image encryption based on Arnold transform and Sine Chaotification Model. Hu [14] proposed a quantum image encryption algorithm based on Arnold scrambling and wavelet transforms.…”

A Tweakable Image Encryption Algorithm Using an Improved Logistic Chaotic Map

“…For this reason, image encryption has become an urgent challenge and high concern which has attracted many researchers in recent years. Several image encryption schemes have been developed using diverse techniques such as quantum theory [1,2], DNA coding [3,4], chaos theory [5][6][7][8][9][10][11][12], and very recently Hua et al [13] have developed an image encryption algorithm with Josephus shuffling and filtering diffusion, to get better diffusion effectiveness. The introduction of chaos in image encryption techniques is mainly due to the good features it offers for image data protection, such as its extremely high sensitivity dependence on initial conditions and control parameters, nonlinearity, ergodicity and random-like behaviors [5].…”

“…The introduction of chaos in image encryption techniques is mainly due to the good features it offers for image data protection, such as its extremely high sensitivity dependence on initial conditions and control parameters, nonlinearity, ergodicity and random-like behaviors [5]. In addition, the security of an image encryption scheme that uses a chaotic map output sequences usually depends on two parts, namely, the permutation and diffusion processes [1][2][3][4][5]. In the permutation process, the pixel locations of the images are altered to remove the redundancies and break the high correlation among adjacent pixels, while in the diffusion process, the image pixel values are changed.…”