Compressive sensing based image compression-encryption using Novel 1D-Chaotic map

Ponuma, R.; Amutha, R.

doi:10.1007/s11042-017-5378-2

Cited by 97 publications

(37 citation statements)

References 41 publications

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“…The theory of compressed perception was formally proposed by E.J. Candes, in 2004 [17], and already had a definite theoretical foundation in the last century. This technology has been applied in many fields, including image processing, medical imaging, geophysics, computer science, signal processing, etc.…”

Section: Theories and Methodsmentioning

confidence: 99%

Image Parallel Encryption Technology Based on Sequence Generator and Chaotic Measurement Matrix

Guo

Song

et al. 2020

Entropy

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In this paper, a new image encryption transmission algorithm based on the parallel mode is proposed. This algorithm aims to improve information transmission efficiency and security based on existing hardware conditions. To improve efficiency, this paper adopts the method of parallel compressed sensing to realize image transmission. Compressed sensing can perform data sampling and compression at a rate much lower than the Nyquist sampling rate. To enhance security, this algorithm combines a sequence signal generator with chaotic cryptography. The initial sensitivity of chaos, used in a measurement matrix, makes it possible to improve the security of an encryption algorithm. The cryptographic characteristics of chaotic signals can be fully utilized by the flexible digital logic circuit. Simulation experiments and analyses show that the algorithm achieves the goal of improving transmission efficiency and has the capacity to resist illegal attacks.

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Section: Theories and Methodsmentioning

confidence: 99%

Image Parallel Encryption Technology Based on Sequence Generator and Chaotic Measurement Matrix

Guo

Song

et al. 2020

Entropy

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“…From Table. 5 Proposed algorithms scored a very close NPCR and UACI values compared to other schemes with the advantage of watermarking capability. Table. 6 shows PSNR of the test images Baboon, Boat, Lake, Man and Peppers compared with the schemes proposed in [35] and [36]. It is noticeable that proposed algorithm achieves high PSNR compared to other schemes which leads to better reconstructed image quality.…”

Section: Encryption Analysismentioning

confidence: 97%

“…To do that, it costs proposed algorithm expansion rate up to 31%. Table. 4 lists PSNR and compression ratio for proposed algorithm, scheme proposed in [7] and scheme proposed in [35]. According to Table. 4, it is evident that the proposed algorithm outperforms the scheme proposed in [7] and [35] in terms of compression ratio and PSNR.…”

Section: Compression Analysismentioning

confidence: 99%

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Joint Image Watermarking, Compression and Encryption Based on Compressed Sensing and Entropy Coding

Mostafa¹,

Fakhr²

2018

IJMA

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Image usage over the internet becomes more and more important each day. Over 3 billion images are shared each day over the internet which raise a concern about how to protect images copyrights? Or how to utilize image sharing experience? This paper proposes a new robust image watermarking algorithm based on compressed sensing (CS) and quantization index modulation (QIM) watermark embedding. The algorithm capitalizes on the CS to compress and encrypt images jointly with Entropy Coding, Arnold Cat Map, Pseudo-random numbers and Advanced Encryption Standard (AES). Our proposed algorithm works under the JPEG standard umbrella. Watermark embedding is done in 3 different locations inside the image using QIM. Those locations differ with each 8-by-8 image block. Choosing which combination of coefficients to be used in QIM watermark embedding depends on selecting a combination from combinations table, which is generated at the same time with projection matrices using a 10-digits Pseudorandom number secret key SK 1. After quantization phase, the algorithm shuffles image blocks using Arnold's Cat Map with a 10-digits Pseudo-random number secret key SK 2 , followed by a unique method for splitting every 8x8 block into two unequal parts. Part number one will act as the host for two QIM watermarks then goes through encoding phase using Run-Length Encoding (RLE) followed by Huffman Encoding, while part number two goes through sparse watermark embedding followed by a third QIM watermark embedding and compression phase using CS, then Huffman encoder is used to encode this part. The algorithm aims to combine image watermarking, compression and encryption capabilities in one algorithm while balancing how those capabilities works with each other to achieve significant improvement in terms of image watermarking, compression and encryption. 15 different images usually used in image processing benchmarking were used for testing the algorithm capabilities and experiments show that our proposed algorithm achieves robust watermarking jointly with encryption and compression under the JPEG standard framework.

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“…In the information field, the traditional informationcapturing paradigm always follows the well-known Shannon-Nyquist sampling theory (Ponuma & Amutha, 2017), which states that the signal sampling rate reaches more than twice the signal bandwidth. This paradigm increases the storage consumption and transmission bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Construction of Chaos-Gaussian measurement matrix based on the even cross-correlation properties of logistic sequence

Kong

et al. 2018

Systems Science & Control Engineering

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Compressive sensing based image compression-encryption using Novel 1D-Chaotic map

Cited by 97 publications

References 41 publications

Image Parallel Encryption Technology Based on Sequence Generator and Chaotic Measurement Matrix

Image Parallel Encryption Technology Based on Sequence Generator and Chaotic Measurement Matrix

Joint Image Watermarking, Compression and Encryption Based on Compressed Sensing and Entropy Coding

Construction of Chaos-Gaussian measurement matrix based on the even cross-correlation properties of logistic sequence

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