A Novel Lightweight Image Encryption Scheme
Source abstract
Abstract
Encryption algorithms are one of the methods to protect data during its transmission through an unsafe transmission medium. But encryption methods need a lot of time during encryption and decryption, so it is necessary to find encryption algorithms that consume little time while preserving the security of the data. In this paper, more than one algorithm was combined to obtain high security with a short implementation time. A chaotic system, DNA computing, and Salsa20 were combined. A proposed 5D chaos system was used to generate more robust keys in a Salsa algorithm and DNA computing. Also, the confusion is performed using a new S-Box. The proposed chaos system achieves three positive Lyapunov values. So results demonstrate of the proposed scheme has a sufficient peak signal-to-noise ratio, a low correlation, and a large key space. These factors make it more efficient than its classical counterpart and can resist statistical and differential attacks. The number of changing pixel rates (NPCR) and the unified averaged changed intensity (UACI) values were 0.99710 and UACI 33.68. The entropy oscillates from 7.9965 to 7.9982 for the tested encrypted images. The suggested approach is resistant to heavy attacks and takes less time to execute than previously discussed methods, making it an efficient, lightweight image encryption scheme. The method provides lower correlation coefficients than other methods, another indicator of an efficient image encryption system. Even though the proposed scheme has useful applications in image transmission, it still requires profound improvement in implementing the high-intelligence scheme and verifying its feasibility on devices with the Internet of Things (IoT) enabled.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Chaos-based Image/Signal Encryption · Cryptographic Implementations and Security · Advanced Steganography and Watermarking Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
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