m+Medelevator.RU
← Поиск публикаций
Scientific reports · 2026

A quantum resistant chaos driven image encryption framework for secure visual data transmission in intelligent transportation systems.

СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Prajwalasimha S N, Sharma Anurag, Saini Dilip Kumar Jang Bahadur, Rai Bipin Kumar, Kumar Gautam, Chakrabarti Prasun

Аннотация

The use of image-sensing and real-time processing in Intelligent Transportation Systems (ITS) has introduced a sudden surge in transmitting and gathering high-resolution visual data from vehicle cameras, road infrastructures, and user devices. Such image data are, however, exceedingly susceptible to interception, tampering, and privacy breaches with regard to imminent quantum computing attacks that can break classical encryption algorithms. With such constraints in view, the paper presents a new Hybrid Quantum-Classical Image Encryption Framework that integrates chaos-based bit-level image encryption and quantum-resistant encryption measures to ensure high-security protection of image information in ITS infrastructures. The new framework integrates a customized bit-level chaotic permutation scheme using a Rearranged Arnold Cat Map (R-ACM) and 2D Logistic-Sine Chaotic Maps for confusion and diffusion, and the inclusion of a Quantum Key Distribution (QKD) or post-quantum lattice-based Kyber Key Encapsulation Mechanism (KEM) for secure key negotiation. The two-pyramidal security architecture enhances sensitivity to key and plaintext variations, offers chosen-plaintext, differential, noise, and occlusion attack immunity, and supports efficient encryption of RGB and grayscale image information without excessively large time overhead. Experimental results on representative ITS-relevant image data sets verify superior performance with mean NPCR > 99.60%, UACI ≈ 33.5%, entropy measures close to 8.0, and significantly suppressed correlation between neighboring pixels. Further, key space analysis demonstrates a combinatorial complexity of over 2²⁵⁶, making brute-force and quantum-type attacks computationally infeasible. The new framework is extremely suitable for real-time implementation in autonomous vehicles, roadside edge nodes, and intelligent traffic monitoring systems, thereby enabling secure, intelligent, and privacy-preserving ITS infrastructure in the post-quantum era.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты