LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

An Efficient Multi-Secret Image Sharing System Based on Chinese Remainder Theorem and Its FPGA Realization

Photo by saadahmad_umn from unsplash

Multi-Secret Image Sharing (MSIS) is important in information security when multiple images are shared in an unintelligible form to different participants, where the images can only be recovered using the… Click to show full abstract

Multi-Secret Image Sharing (MSIS) is important in information security when multiple images are shared in an unintelligible form to different participants, where the images can only be recovered using the shares from participants. This paper proposes a simple and efficient ( $n,n$ )-MSIS system for colored images based on XOR and Chinese Remainder Theorem (CRT), where all the $n$ share are required in the recovery. The system improves the security by adding dependency on the input images to be robust against differential attacks, and by using several delay units. It works with even and odd number of inputs, and has a long sensitive system key design for the CRT. Security analysis and a comparison with related literature are introduced with good results including statistical tests, differential attack measures, and key sensitivity tests as well as performance analysis tests such as time and space complexity. In addition, Field Programmable Gate Array (FPGA) realization of the proposed system is presented with throughput 530 Mbits/sec. Finally, the proposed MSIS system is validated through software and hardware with all statistical analyses and proper hardware resources with low power consumption, high throughput and high level of security.

Keywords: system; tex math; inline formula; secret image; multi secret; image sharing

Journal Title: IEEE Access
Year Published: 2023

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.