Chaotic Encryption Algorithm With Key Controlled Neural Networks for Intelligent Transportation Systems
| Autor: | Radu Muresan, Arafat Al-Dweik, Graham R. W. Thoms |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
cryptography
General Computer Science Artificial neural network Computer science business.industry intelligent transportation systems General Engineering Chaotic Cryptography chaotic systems image encryption Encryption law.invention law Robustness (computer science) General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering finite fields Cryptanalysis business Intelligent transportation system Private information retrieval Algorithm lcsh:TK1-9971 Neural network encryption |
| Zdroj: | IEEE Access, Vol 7, Pp 158697-158709 (2019) |
| ISSN: | 2169-3536 |
| Popis: | The security of sensitive information is vital in many aspects of multimedia applications such as Intelligent Transportation Systems (ITSs), where traffic data collection, analysis and manipulations is essential. In ITS, the images captured by roadside units form the basis of many traffic rerouting and management techniques, and hence, we should take all precautions necessary to deter unwanted traffic actions caused by malicious adversaries. Moreover, the collected traffic images might reveal critical private information. Consequently, this paper presents a new image encryption algorithm, denoted as ChaosNet, using chaotic key controlled neural networks for integration with the roadside units of ITSs. The encryption algorithm is based on the Lorenz chaotic system and the novel key controlled finite field neural network. The obtained cryptanalysis show that the proposed encryption scheme has substantial mixing properties, and thus cryptographic strength with up to 5% increase in information entropy compared to other algorithms. Moreover, it offers consistent resistance to common attacks demonstrated by nearly ideal number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), pixel correlation coefficient values, and robustness to cropped attacks. Furthermore, it has less than 0.002% difference in the NPCR and 0.3% in the UACI metrics for different test images. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|