An efficient image encryption algorithm using 3D-cyclic chebyshev map and elliptic curve
With the development of internet technologies, it is now usual to communicate enormous amounts of text and visual data over networks, which calls for stringent procedures to guarantee confidentiality and integrity throughout transmission. An important part of safeguarding image communication over secure channel is cryptography. This study proposes an effective way to secure sensitive data by creating tamper-proof cryptosystems and authentication mechanisms. By employing elliptic curve cryptography (ECC) to generate secret encryption key and chaotic maps to successfully disguise and alter plain image pixel values, the technique strengthens overall system security through the use of hybrid cryptography. The method has a big enough key space to withstand brute-force attempts and exhibits robustness against statistical and differential attacks through analysis including evaluations and assessments of correlation, entropy, and histograms as well as evaluations such as NPCR, UACI, PSNR and keyspace analysis validate the efficacy of the proposed image encryption algorithm, affirming its systematic and robust nature and its capacity to offer superior image encryption protection over some existing ones.
Authors
- Shaha Al‐Otaibi (ORCID: https://orcid.org/0000-0002-6720-9955)
- Muhammad Naeem Ashiq (ORCID: https://orcid.org/0000-0001-8561-3065)
- Saba Inam (ORCID: https://orcid.org/0000-0003-3218-1427)
- Nasir Siddiqui (ORCID: https://orcid.org/0000-0002-0612-4835)
- Shamsa Kanwal (ORCID: https://orcid.org/0000-0001-5236-869X)
- Javaria Akbar
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- University of Engineering and Technology Taxila (PK)
- Fatima Jinnah Women University (PK)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1038/s41598-024-77955-w
- Citations
- 8
- Primary Topic
- Chaos-based Image/Signal Encryption
- Type
- article
- Field-Weighted Citation Impact
- 1.99
Funders
- Princess Nourah Bint Abdulrahman University