A medical image crypto-compression algorithm based on neural network and PWLCM

Hajjaji, Mohamed Ali; Dridi, Manel; Mtibaa, Abdellatif

doi:10.1007/s11042-018-6795-6

Cited by 29 publications

(8 citation statements)

References 18 publications

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“…The frequently used tools for statistical analysis in the image cryptography community are the image histogram, information Entropy (E), correlation coefficient (ρ), and Normalized Correlation (NC). [31][32][33][34] The use of the hardware co-simulation approach allows us to generate the corresponding encrypted image for each image. Afterward, we use the aforementioned tools to analyze the encrypted image.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical analysis provides a variety of mathematical methods for evaluating the ciphered image against statistical hackers. The frequently used tools for statistical analysis in the image cryptography community are the image histogram, information Entropy (E), correlation coefficient (

ρ

), and Normalized Correlation (NC) 31–34 . The use of the hardware co‐simulation approach allows us to generate the corresponding encrypted image for each image.…”

Section: Discussionmentioning

confidence: 99%

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Hardware implementation of a strong pseudorandom number generator based block‐cipher system for color image encryption and decryption

Gafsi

Amdouni

Hajjaji

et al. 2022

Circuit Theory & Apps

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Summary This paper proposed a hardware architecture of a strong block‐cipher system dedicated to digital image encryption and decryption. On the one hand, a pseudorandom number generator (PRNG) based on two 3D chaotic systems is created to produce strong keys. On the other hand, a robust algorithm is proposed to ensure high‐level security and low computational complexity of image encryption. The algorithm performs image encryption mainly through three processes: pixel values hiding by applying the XOR operation with a key, pixel positions hiding by operating random permutation, and pixel substitution using the S‐box method. To increase the complexity, R rounds of encryption could be accomplished in a loop. Then as a final step, using the Xilinx Vivado/system generator tool, the hardware cryptosystem is developed, implemented, and evaluated on an FPGA‐Zynq evaluation board. According to the synthesis results, the suggested hardware system performs on a reduced FPGA area and gives a good frequency of 156.813 MHz with a high throughput of 20,072.064 Mbps. Several tools and tests utilizing various images are used to evaluate and analyze the hardware cryptosystem. The experimental results show that the hardware implementation has higher performance compared to other recent works.

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Section: Discussionmentioning

confidence: 99%

ρ

), and Normalized Correlation (NC) 31–34 . The use of the hardware co‐simulation approach allows us to generate the corresponding encrypted image for each image.…”

Section: Discussionmentioning

confidence: 99%

Hardware implementation of a strong pseudorandom number generator based block‐cipher system for color image encryption and decryption

Gafsi

Amdouni

Hajjaji

et al. 2022

Circuit Theory & Apps

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“…References [ 11 , 12 ] use carrier images transformed by wavelet packets to hide information. References [ 13 , 14 ] proposed an image hiding algorithm in the discrete Fourier transform domain. References [ 15 , 16 ] proposed a DCT algorithm to change the pixel length to achieve image hiding.…”

Section: Related Workmentioning

confidence: 99%

Random Matrix Transformation and Its Application in Image Hiding

Wang

Tan

Yuan

2023

Sensors

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Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality.

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“…Indeed, the resemblance between the original and extracted watermark is detected by the calculus of different distances among the detected and inserted watermark. is measure is performed via the NCC (normalized cross-correlation) [23,37,38]. Its value is generally between 0 and 1.…”

Section: Ncc (Normalized Cross-correlationmentioning

confidence: 99%

Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

Ajili

Hajjaji

Mtibaa

2021

Scientific Programming

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We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient’s data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt the watermarked medical images by using the AES algorithm based on chaotic technique. Arnold’s cat map is used to shuffle the pixel values, and a chaotic Henon map is utilized to generate an aleatory sequence for the AES algorithm. The shuffled watermarked image is encrypted using the modified AES algorithm. The constant of Weber is used to choose the suitable visibility factor for embedding a watermark with high robustness. To control identification, after application of attacks, we use the serial turbo code for correction of the watermark to recover the data inserted. The average peak signal-to-noise ratio (PSNR) of the medical images obtained is 61,7769 dB. Experimental results demonstrate the robustness of the proposed schema against various types of attacks.

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A medical image crypto-compression algorithm based on neural network and PWLCM

Cited by 29 publications

References 18 publications

Hardware implementation of a strong pseudorandom number generator based block‐cipher system for color image encryption and decryption

Hardware implementation of a strong pseudorandom number generator based block‐cipher system for color image encryption and decryption

Random Matrix Transformation and Its Application in Image Hiding

Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

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