Compression and Confusion Scheme with Perceptual Security for Smart Cities Applications

Compression and Confusion Scheme with Perceptual Security for Smart Cities Applications

연구 분야: Analysis

학회: SN Computer Science

In the realm of smart cities, where the proliferation of surveillance systems and security cameras is steadily increasing, the effective handling of digital images and safeguarding their confidentiality has emerged as a crucial challenge. In this work, we present an innovative scheme composed of three main components. Firstly, compressive sensing is employed for image compression. Secondly, S-Boxes are utilized for enhancing security. Finally, a Pseudo Random Number Generator based on Arnold’s cat map is used to achieve the confusion property within the context of Shannon’s information theory. The integration of these three building blocks provides a comprehensive solution for the secure transmission of image data in urban environments. Our scheme expands the compressed image into a 24-bit RGB image and uses the S-Boxes to replace the information of each color channel. A set of substitution boxes is stored, and one is selected in each round, using the Pseudo Random Number Generator. In this regard, the scheme provides a solution for smart cities seeking to enhance the management of digital image data while also ensuring the security of transmitted information. Obtained results demonstrates that our scheme delivers both perceptual and cryptographic security to digital images, without compromising the quality of the recovered image. This is a numerical implementation, but given the level of security provided, a future optimized version could be implemented in surveillance cameras within smart city urban environments.