An Effective Approach to Scramble Multiple Diagnostic Imageries Using Chaos-Based Cryptography
- URL: http://arxiv.org/abs/2406.07560v1
- Date: Thu, 2 May 2024 05:18:46 GMT
- Title: An Effective Approach to Scramble Multiple Diagnostic Imageries Using Chaos-Based Cryptography
- Authors: Dr Chandra Sekhar Sanaboina, Tejaswini Yadla,
- Abstract summary: We provide a chaotic system-based medical picture encryption method.
The permutation based on plain image and chaotic keys is offered to shuffle the plain picture's pixels to other rows and columns.
We analyze the chaotic behavior of the proposed system using various techniques and tests such as bifurcation plots, Lyapunov exponents, MSE, PSNR tests, and histogram analysis.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Medical image encryption could aid in preserving patient privacy. In this article, we provide a chaotic system-based medical picture encryption method. The diffusion and permutation architecture was used. The permutation based on plain image and chaotic keys is offered to shuffle the plain picture's pixels to other rows and columns, weakening the strong connections between neighboring pixels. Diffusion is suggested to spread small changes of plain images to all of the pixels in cipher images to enhance the encryption effect. We analyze the chaotic behavior of the proposed system using various techniques and tests such as bifurcation plots, Lyapunov exponents, MSE, PSNR tests, and histogram analysis.
Related papers
- Encryption of Audio Signals Using the Elzaki Transformation and the Lorenz Chaotic System Lorenz Chaotic System [0.0]
Several cryptographic techniques have been particularly designed to ensure the privacy of digital images.
This study presents a novel method for encrypting color images utilizing chaos theory and a special transformation.
arXiv Detail & Related papers (2024-09-21T10:13:17Z) - Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network.
Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
arXiv Detail & Related papers (2024-08-28T10:07:17Z) - Deep Learning and Chaos: A combined Approach To Image Encryption and Decryption [1.8749305679160366]
We introduce a novel image encryption and decryption algorithm using hyperchaotic signals from the novel 3D hyperchaotic map, 2D memristor map, Convolutional Neural Network (CNN)
The robustness of the encryption algorithm is shown by key sensitivity analysis, i.e., the average sensitivity of the algorithm to key elements.
arXiv Detail & Related papers (2024-06-24T16:56:22Z) - Perceptual Image Compression with Cooperative Cross-Modal Side
Information [53.356714177243745]
We propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff.
Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features.
arXiv Detail & Related papers (2023-11-23T08:31:11Z) - PermutEx: Feature-Extraction-Based Permutation -- A New Diffusion Scheme for Image Encryption Algorithms [2.2351927942921366]
This paper introduces PermutEx, a feature-extraction-based permutation method that scrambles pixels effectively.
The method effectively disrupts the correlation in information-rich areas within the image resulting in a correlation value of 0.000062.
arXiv Detail & Related papers (2023-11-05T23:46:25Z) - Human-imperceptible, Machine-recognizable Images [76.01951148048603]
A major conflict is exposed relating to software engineers between better developing AI systems and distancing from the sensitive training data.
This paper proposes an efficient privacy-preserving learning paradigm, where images are encrypted to become human-imperceptible, machine-recognizable''
We show that the proposed paradigm can ensure the encrypted images have become human-imperceptible while preserving machine-recognizable information.
arXiv Detail & Related papers (2023-06-06T13:41:37Z) - A novel conservative chaos driven dynamic DNA coding for image
encryption [0.0]
The proposed image encryption algorithm is a dynamic DNA coding algorithm.
The results are promising and prove the robustness of the algorithm against various common cryptanalytic attacks.
arXiv Detail & Related papers (2022-07-12T11:40:09Z) - Proxy-bridged Image Reconstruction Network for Anomaly Detection in
Medical Images [59.700111685673846]
Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set.
We propose a novel Proxy-bridged Image Reconstruction Network ( ProxyAno) for anomaly detection in medical images.
arXiv Detail & Related papers (2021-10-05T00:40:43Z) - Randomness Evaluation of a Genetic Algorithm for Image Encryption: A
Signal Processing Approach [7.310043452300736]
The GFHT cipher is a genetic algorithm that combines gene fusion (GF) and horizontal gene transfer (HGT) both inspired from antibiotic resistance in bacteria.
The encryption starts by a GF of the principal key-agent in a single block, then HGT performs obfuscation where the genes are pixels and the chromosomes are the rows and columns.
arXiv Detail & Related papers (2020-08-09T07:50:29Z) - Improved Slice-wise Tumour Detection in Brain MRIs by Computing
Dissimilarities between Latent Representations [68.8204255655161]
Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods.
We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder.
We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
arXiv Detail & Related papers (2020-07-24T14:02:09Z) - Modeling Lost Information in Lossy Image Compression [72.69327382643549]
Lossy image compression is one of the most commonly used operators for digital images.
We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
arXiv Detail & Related papers (2020-06-22T04:04:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.