Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing

• URL: http://arxiv.org/abs/2309.11471v1
• Date: Wed, 20 Sep 2023 17:11:35 GMT
• Title: Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing
• Authors: Laiba Asghar, Fawad Ahmed, Muhammad Shahbaz Khan, Arshad Arshad, Jawad Ahmad,
• Abstract summary: Noise-Crypt is an image encryption algorithm that integrates non-linear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The proposed scheme has been evaluated for several security parameters, such as differential attacks, entropy, correlation, etc. Results of the security analysis validate the potency of the proposed scheme in achieving robust image encryption.
• Score: 0.8205507411993582
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To secure the digital images over insecure transmission channels, a new image encryption algorithm Noise-Crypt is proposed in this paper. Noise-Crypt integrates non-linear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The utilized hybrid chaotic maps are the logistic-tent and the logistic-sine-cosine map. The hybrid chaotic maps enhance the pseudorandom sequence generation and selection of substitution boxes, while the logistic-sine-cosine map induces non-linearity in the algorithm through random noise. This deliberate inclusion of noise contributes to increased resistance against cryptanalysis. The proposed scheme has been evaluated for several security parameters, such as differential attacks, entropy, correlation, etc. Extensive evaluation demonstrates the efficacy of the proposed scheme, with almost ideal values of entropy of 7.99 and correlation of -0.0040. Results of the security analysis validate the potency of the proposed scheme in achieving robust image encryption.

Related papers

• A robust image encryption scheme based on new 4-D hyperchaotic system and elliptic curve [1.2499537119440245]
  A new 4-D hyperchaotic system for image encryption is proposed and its effectiveness is demonstrated. The proposed system is considered simple because it consists of eight terms with two nonlinearities. The two-stage encryption process, involving confusion and diffusion, is employed to protect the confidentiality of digital images.
  arXiv  Detail & Related papers  (2024-11-26T18:08:39Z)
• Multi-Layered Security System: Integrating Quantum Key Distribution with Classical Cryptography to Enhance Steganographic Security [0.0]
  We present a novel cryptographic system that integrates Quantum Key Distribution (QKD) with classical encryption techniques. Our approach leverages the E91 QKD protocol to generate a shared secret key between communicating parties. This key is then hashed using the Secure Hash Algorithm (SHA) to provide a fixedlength, high-entropy key.
  arXiv  Detail & Related papers  (2024-08-13T15:20:29Z)
• Cryptographically Secure Pseudo-Random Number Generation (CS-PRNG) Design using Robust Chaotic Tent Map (RCTM) [2.0448353403141515]
  This paper presents a novel method to generate cryptographically secure pseudo-random numbers (CSPRNG) using a robust chaotic tent map (RCTM) Various statistical tests are performed that ascertain the randomness of generated secure pseudo-random bits.
  arXiv  Detail & Related papers  (2024-08-10T15:16:00Z)
• 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)
• 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)
• Guided Diffusion Model for Adversarial Purification [103.4596751105955]
  Adversarial attacks disturb deep neural networks (DNNs) in various algorithms and frameworks. We propose a novel purification approach, referred to as guided diffusion model for purification (GDMP) On our comprehensive experiments across various datasets, the proposed GDMP is shown to reduce the perturbations raised by adversarial attacks to a shallow range.
  arXiv  Detail & Related papers  (2022-05-30T10:11:15Z)
• Improved, Deterministic Smoothing for L1 Certified Robustness [119.86676998327864]
  We propose a non-additive and deterministic smoothing method, Deterministic Smoothing with Splitting Noise (DSSN) In contrast to uniform additive smoothing, the SSN certification does not require the random noise components used to be independent. This is the first work to provide deterministic "randomized smoothing" for a norm-based adversarial threat model.
  arXiv  Detail & Related papers  (2021-03-17T21:49:53Z)
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
  We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
  arXiv  Detail & Related papers  (2021-01-08T02:03:25Z)
• Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
  We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
  arXiv  Detail & Related papers  (2020-11-18T16:40:45Z)
• WaveTransform: Crafting Adversarial Examples via Input Decomposition [69.01794414018603]
  We introduce WaveTransform', that creates adversarial noise corresponding to low-frequency and high-frequency subbands, separately (or in combination) Experiments show that the proposed attack is effective against the defense algorithm and is also transferable across CNNs.
  arXiv  Detail & Related papers  (2020-10-29T17:16:59Z)
• Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution [70.78655569298923]
  Integrated Gradients as an attribution method for deep neural network models offers simple implementability. It suffers from noisiness of explanations which affects the ease of interpretability. The SmoothGrad technique is proposed to solve the noisiness issue and smoothen the attribution maps of any gradient-based attribution method.
  arXiv  Detail & Related papers  (2020-04-22T10:43:19Z)

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.