Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Gong, Li-Hua; Deng, Chengzhi; Pan, Shumin; Zhou, Nanrun

doi:10.1016/j.optlastec.2018.01.007

Cited by 91 publications

(32 citation statements)

References 32 publications

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“…The SPIHT algorithm involves two coding passes: the sorting pass and the refinement pass. The sorting pass looks for zero trees and sorts significant and insignificant coefficients with respect to a specified threshold, as shown in Equation (5). The refinement pass sends the precision bits of the significant coefficients.…”

Section: Spiht-based Image Compressionmentioning

confidence: 99%

“…Many encryption algorithms, specifically for digital images, have been proposed to meet the requirements of robust image encryption applications. Among them, the chaos-based encryption algorithms are popular due to several excellent properties, such as ergodicity, randomness, unpredictability, and high sensitivity to initial conditions and various parameters [5][6][7].…”

Section: Image Encryption With Chaotic Mapsmentioning

confidence: 99%

“…Therefore, specific encryption techniques are required for such a bulky data. In comparison to traditional encryption techniques, chaotic map cryptosystems are considered more suitable for image encryption [3][4][5][6][7][8][9][10][11][12]. However, some chaotic cryptosystems have well-known weaknesses such as relatively small key size, short-length chaotic orbits and lack of the confusion property [4,13].…”

Section: Introductionmentioning

confidence: 99%

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Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps

Nasrullah¹,

Sang

Akbar

et al. 2018

Applied Sciences

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Confidentiality and efficient bandwidth utilization require a combination of compression and encryption of digital images. In this paper, a new method for joint image compression and encryption based on set partitioning in hierarchical trees (SPIHT) with optimized Kd-tree and multiple chaotic maps was proposed. First, the lossless compression and encryption of the original images were performed based on integer wavelet transform (IWT) with SPIHT. Wavelet coefficients undergo diffusions and permutations before encoded through SPIHT. Second, maximum confusion, diffusion and compression of the SPIHT output were performed via the modified Kd-tree, wavelet tree and Huffman coding. Finally, the compressed output was further encrypted with varying parameter logistic maps and modified quadratic chaotic maps. The performance of the proposed technique was evaluated through compression ratio (CR) and peak-signal-to-noise ratio (PSNR), key space and histogram analyses. Moreover, this scheme passes several security tests, such as sensitivity, entropy and differential analysis tests. According to the theoretical analysis and experimental results, the proposed method is more secure and decreases the redundant information of the image more than the existing techniques for hybrid compression and encryption.

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Section: Spiht-based Image Compressionmentioning

confidence: 99%

Section: Image Encryption With Chaotic Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps

Nasrullah¹,

Sang

Akbar

et al. 2018

Applied Sciences

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show abstract

“…On the other hand, the surveillance system generates a huge amount of data that require realtime processing. To deal with computational complexity, some researchers propose several contributions such as video summarization techniques [11,12]. e main task of these techniques is to summarize the most informative parts of video data.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

et al. 2019

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Surveillance systems paradigm envisions the pervasive interconnection and cooperation of interactive devices over the Internet infrastructure. Nevertheless, dissemination and processing of surveillance video amid the Internet of Things (IoT) applications become a susceptible issue due to the large volume and the significant information of these data. Moreover, surveillance devices on IoT have very limited resources such as memory and storage. The actual security methods are not quite appropriate for surveillance IoT systems. Thus, a particular cryptosystem technique is required for surveillance data security. In this paper, we propose an efficient cryptosystem to secure IoT-based surveillance systems. The proposed cryptosystem framework contains three parts. First, a lightweight automatic summarization technique based on a fast histogram-clustering approach is used to extract the keyframes from the surveillance video. Then, we employ a discrete cosine transform (DCT) technique to compress the extracted data size. Finally, the proposed framework performs an efficient image encryption algorithm by employing a discrete fractional random transform (DFRT). The testing results and analysis confirm the features of the proposed cryptosystem on surveillance systems. The proposed framework is fast and ensures secure and efficient real-time processing by minimizing the transmission cost and storage.

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“…Nevertheless, the security of the low-dimensional chaotic system is a little weak for its disadvantage of short code period. Researchers pay more attention to more complex chaotic systems, such as the high-dimensional chaotic systems and compound chaotic systems [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard

Zhang

Wang

2018

Applied Sciences

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With the increasing use of multimedia in communications, the content security of remote-sensing images attracts much attention in both the academia and industry. The Advanced Encryption Standard (AES) is a famous symmetric cryptosystem. A symmetric remote-sensing image encryption algorithm using AES is presented. Firstly, to reduce the encryption times, the sender groups 16 pixel values together, and converts them into big integers; secondly, the sender encrypts big integers with AES and the chaotic system; finally, the encrypted image is obtained from encrypted big integers. Simulation data show that our algorithm exhibits both the high security and efficiency.

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Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Cited by 91 publications

References 32 publications

Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps

Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps

An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard

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