Abstract
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.
Highlights
Advanced intelligent transportation systems (ITSs) are one of the main driving technologies of smart city development, becoming a pillar of city infrastructure as population and autonomous vehicle developments continue to grow [1]
This paper presents the development of an image encryption algorithm, ChaosNet, utilizing chaotic systems and key-controlled neural networks for use in the Scalable Enhanced Roadside Unit (SERSU) [2] and other ITS applications
This paper develops an image encryption algorithm based on the Lorenz chaotic system and chained finite field transformation layers to form a neural-network-like structure, which in conjunction with a novel full-image permutation scheme will produce an encryption algorithm that makes cipherimages unintelligible and highly secure
Summary
Advanced intelligent transportation systems (ITSs) are one of the main driving technologies of smart city development, becoming a pillar of city infrastructure as population and autonomous vehicle developments continue to grow [1]. In this sequel, this paper presents the development of an image encryption algorithm, ChaosNet, utilizing chaotic systems and key-controlled neural networks for use in the Scalable Enhanced Roadside Unit (SERSU) [2] and other ITS applications. Chaotic maps have been an attractive basis for cryptographic applications, due to the hyper-sensitivity to initial conditions and input parameters, producing pseudorandom and unpredictable behavior [3] The generation of these new chaos-based encryption schemes mainly focus on an image as the input, since many chaotic maps provide thorough topologically mixing properties which are well suited for the two-dimensional nature of images [4]–[20]. The most important indicator for a system to be chaotic is to have at least one positive exponent within the set of resulting Lyapunov exponents, which indicates trajectory divergence and quantifies dependence on initial conditions by showing the rate at which two close points diverge over time [27]
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