Abstract
In this modernistic age of innovative technologies like big data processing, cloud computing, and Internet of things, the utilization of multimedia information is growing daily. In contrast to other forms of multimedia, videos are extensively utilized and streamed over the Internet and communication networks in numerous Internet of Multimedia Things (IoMT) applications. Consequently, there is an immense necessity to achieve secure video transmission over modern communication networks due to the third-party exploitation and falsification of transmitted and stored digital multimedia data. The present methods for secure communication of multimedia content between clouds and mobile devices have constraints in terms of processing load, memory support, data size, and battery power. These methods are not the optimum solutions for large-sized multimedia content and are not appropriate for the restricted resources of mobile devices and clouds. The High-Efficiency Video Coding (HEVC) is the latest and modern video codec standard introduced for efficiently storing and streaming of high-resolution videos with suitable size and higher quality. In this paper, a novel hybrid cryptosystem combining DNA (Deoxyribonucleic Acid) sequences, Arnold chaotic map, and Mandelbrot sets is suggested for secure streaming of compressed HEVC streams. Firstly, the high-resolution videos are encoded using the H.265/HEVC codec to achieve efficient compression performance. Subsequently, the suggested Arnold chaotic map ciphering process is employed individually on three channels (Y, U, and V) of the compressed HEVC frame. Then, the DNA encoding sequences are established on the primary encrypted frames resulted from the previous chaotic ciphering process. After that, a modified Mandelbrot set-based conditional shift process is presented to effectively introduce confusion features on the Y, U, and V channels of the resulted ciphered frames. Massive simulation results and security analysis are performed to substantiate that the suggested HEVC cryptosystem reveals astonishing robustness and security accomplishment in contrast to the literature cryptosystems.
Highlights
Internet of Things (IoT) systems have enormous computation and processing costs, and deliver massive amounts of multimedia data, upon storage utilizing cloudThe associate editor coordinating the review of this manuscript and approving it for publication was Parul Garg.systems [1]
Motivated by the preceding debates, to tackle such drawbacks, this paper introduces a novel hybrid High-Efficiency Video Coding (HEVC) cryptosystem amalgamating Arnold chaotic map, DNA functions, and modified Mandelbrot set
The reference HEVC Test Model (HM) codec [4] is firstly employed to encode the tested H.265 video streams to generate the compressed HEVC frames that will be considered as the input for the suggested HEVC cryptosystem
Summary
Internet of Things (IoT) systems have enormous computation and processing costs, and deliver massive amounts of multimedia data, upon storage utilizing cloudThe associate editor coordinating the review of this manuscript and approving it for publication was Parul Garg.systems [1]. Compressed Videos various intersecting observations by continuously gaining video frames, creating a huge amount of multimedia data with considerable redundancy. It is commonly approved in the research community of multimedia communication applications and services that the collected multimedia data should be pre-processed to obtain the important and informative content before multimedia streaming [3]. It is unpreferable to transmit the visual data through the communication channels without processing (e.g. compression), this is unrealistic due to energy and bandwidth limitations. There is a mandatory need for an efficient compression process for multimedia data before their streaming over bandwidth-limited communication channels. In contrast with its antecedent H.264/AVC (Advanced Video Coding) video codec, the HEVC codec accomplishes 50% compression ratio with great bit rate reduction by exploiting its improved prediction features of temporal and spatial estimation processes [5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
