Enhanced cast-128 with adaptive s-box optimization via neural networks for image protection
- URL: http://arxiv.org/abs/2509.07606v1
- Date: Tue, 09 Sep 2025 11:29:57 GMT
- Title: Enhanced cast-128 with adaptive s-box optimization via neural networks for image protection
- Authors: Fadhil Abbas Fadhil, Maryam Mahdi Alhusseini, Mohammad-Reza Feizi-Derakhshi,
- Abstract summary: The study aims to address the drawbacks of static S-box models commonly used in traditional cryptographic systems.<n>In the proposed scheme, the dynamic, non-linear, invertible, and highly cryptographic strength S-boxes are generated through a hybrid chaotic system.<n>The system may be applied to secure communications, surveillance systems, and medical image protection on a real-time basis.
- Score: 1.3318026799252651
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: An improved CAST-128 encryption algorithm, which is done by implementing chaos-based adaptive S-box generation using Logistic sine Map (LSM), has been provided in this paper because of the increasing requirements of efficient and smart image encryption mechanisms. The study aims to address the drawbacks of static S-box models commonly used in traditional cryptographic systems, which are susceptible to linear and differential attacks. In the proposed scheme, the dynamic, non-linear, invertible, and highly cryptographic strength S-boxes are generated through a hybrid chaotic system that may have high non-linearity, strong and rigorous avalanche characteristics, and low differential uniformity. The process here is that the LSM is used to produce S-boxes having key-dependent parameters that are stuffed into the CAST-128 structure to encrypt the image in a block-wise manner. The performance of the encryption is assessed utilizing a set of standard grayscale images. The metrics that are used to evaluate the security are entropy, NPCR, UACI, PSNR, and histogram analysis. Outcomes indicate that randomness, resistance to statistical attacks, and country of encryption are significantly improved compared to the original CAST-128. The study is theoretically and practically relevant since it presents a lightweight S-box generation approach driven by chaos, which can increase the level of robustness of the image encryptions without enlisting machine learning. The system may be applied to secure communications, surveillance systems, and medical image protection on a real-time basis.
Related papers
- AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers [69.56534335936534]
AmbShield is an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers'<n>In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices.
arXiv Detail & Related papers (2026-01-14T20:56:50Z) - Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detection [54.433899174017185]
Out-of-distribution (OOD) detection is crucial for building reliable machine learning models.<n>We propose a novel method called Knowledge Regularized Negative Feature Tuning (KR-NFT)<n>NFT applies distribution-aware transformations to pre-trained text features, effectively separating positive and negative features into distinct spaces.<n>When trained with few-shot samples from ImageNet dataset, KR-NFT not only improves ID classification accuracy and OOD detection but also significantly reduces the FPR95 by 5.44%.
arXiv Detail & Related papers (2025-07-26T07:44:04Z) - A Dual-Layer Image Encryption Framework Using Chaotic AES with Dynamic S-Boxes and Steganographic QR Codes [0.0]
This paper presents a robust image encryption and key distribution framework.<n>It integrates an enhanced AES-128 algorithm with chaos theory and advanced steganographic techniques.<n>It offers a scalable, secure solution for sensitive image transmission in applications such as surveillance, medical imaging, and digital forensics.
arXiv Detail & Related papers (2025-06-16T18:16:14Z) - A Novel Feature-Aware Chaotic Image Encryption Scheme For Data Security and Privacy in IoT and Edge Networks [2.0189209920381774]
Security of image data in the Internet of Things (IoT) and edge networks is crucial.<n>Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices.<n>This paper proposes a novel Feature-Aware Chaotic Image Encryption scheme.
arXiv Detail & Related papers (2025-05-01T15:26:48Z) - Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy [51.028047763426265]
Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information.<n> transmitting semantic-rich data over insecure channels introduces privacy risks.<n>This paper proposes a novel SemCom framework that integrates differential privacy mechanisms to protect sensitive semantic features.
arXiv Detail & Related papers (2025-04-23T08:42:44Z) - Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.<n>These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.<n>We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
arXiv Detail & Related papers (2025-01-25T04:53:36Z) - Parallel Sequence Modeling via Generalized Spatial Propagation Network [80.66202109995726]
Generalized Spatial Propagation Network (GSPN) is a new attention mechanism for optimized vision tasks that inherently captures 2D spatial structures.<n>GSPN overcomes limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a line-scan approach.<n>GSPN achieves superior spatial fidelity and state-of-the-art performance in vision tasks, including ImageNet classification, class-guided image generation, and text-to-image generation.
arXiv Detail & Related papers (2025-01-21T18:56:19Z) - Optimizing Secure Quantum Information Transmission in Entanglement-Assisted Quantum Networks [0.0]
This work addresses issues by integrating Quantum Key Distribution (QKD) with Multi-Layer Chaotic Encryption.<n>The framework offers a future-proof approach for defining secure communication protocols in crucial sectors such as medical treatment, forensic computing, and national security.
arXiv Detail & Related papers (2025-01-17T00:51:37Z) - 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) - 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) - Limited-angle tomographic reconstruction of dense layered objects by
dynamical machine learning [68.9515120904028]
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem.
Regularizing priors are necessary to reduce artifacts by improving the condition of such problems.
We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
arXiv Detail & Related papers (2020-07-21T11:48:22Z)
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.